Spectral matching approaches in hyperspectral image ...

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Spectral matching approaches in hyperspectral image processing a

S. Shanmugam & P. SrinivasaPerumal

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Department of Geology, Anna University, Chennai, India Published online: 04 Dec 2014.

Click for updates To cite this article: S. Shanmugam & P. SrinivasaPerumal (2014) Spectral matching approaches in hyperspectral image processing, International Journal of Remote Sensing, 35:24, 8217-8251, DOI: 10.1080/01431161.2014.980922 To link to this article: http://dx.doi.org/10.1080/01431161.2014.980922

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International Journal of Remote Sensing, 2014 Vol. 35, No. 24, 8217–8251, http://dx.doi.org/10.1080/01431161.2014.980922

REVIEW ARTICLE Spectral matching approaches in hyperspectral image processing S. Shanmugam and P. SrinivasaPerumal* Department of Geology, Anna University, Chennai, India


(Received 30 April 2014; accepted 4 October 2014) Many spectral matching algorithms, ranging from the traditional clustering techniques to the recent automated matching models, have evolved. This paper provides a review and up-to-date information on the past and current role of the spectral matching approaches adopted in hyperspectral satellite image processing. The need for spectral matching has been deliberated and a list of spectral matching algorithms has been compared and described. A review of the conventional spectral angle measures and the advanced automated spectral matching tools indicates that, for better performance of target detection, there is a need for combining two or more spectral matching techniques. From the studies of several authors, it is inferred that continuous improvement in the matching techniques over the past few years is due to the need to handle and analyse hyperspectral image data for various applications. The need to develop a wellbuilt and specialized spectral library to accommodate the resources from enormous spectral data is suggested. This may improve accuracy in mineral and soil mapping, vegetation species identification and health monitoring, and target detection. The future role of cloud computing in accessing globally distributed spectral libraries and performing spectral matching is highlighted. Rather than inferring that a particular matching algorithm is the best, this paper points out the requirements of an ideal algorithm. With increasing usage of hyperspectral data for resources mapping, the review presented in this paper will certainly benefit the large and emerging community of hyperspectral image users.

1. Introduction Spectral research is the detailed analysis and interpretation of the spectra of materials. Spectral signatures define the characteristics of objects based on their absorptance, reflectance, and transmittance of electromagnetic radiation. Several spectroscopic techniques are available to interpret such spectral signatures. With the advent of hyperspectral data, the dimension of research on spectral signatures and spectral matching is undergoing immense improvization. Govender, Chetty, and Bulcock (2007), in their review on hyperspectral remote sensing for vegetation and water studies, stated that although multispectral imagery is useful to discriminate land surface features and landscape patterns, detailed signatures from hyperspectral imagery allow for the identification and characterization of materials. The large amount of spectral data produced by hyperspectral imaging necessitates the development of automated techniques that convert imagery directly to thematic maps (Vishnu, Nidamanuri, and Bremananth 2013). Hyperspectral image data such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Compact Airborne Spectrographic Imagers (CASI) I and II, EO-1 Hyperion, Hyperspectral Digital Imagery Collection Experiment (HYDICE), and Moderate Resolution Imaging Spectroradiometer *Corresponding author. Email: [email protected] © 2014 Taylor & Francis


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(MODIS) are constantly used in spectral matching studies. Besides, specialized hyperspectral data sets for ocean studies (Portable Hyperspectral Imaging Low-Light Spectrometer (PHILLS), Hyperspectral Imager for the Coastal Ocean (HICO)) and for planetary studies (OMEGA/Mars Express, Moon Mineralogy Mapper (M3)) are now in use. With all these advanced developments, there is a need to review the aspects of spectral matching, which is realized as a potential tool for effective utilization of these hyperspectral resources. Hence, this paper aims to provide an extensive review on spectral matching, algorithms and analyses the components and factors influencing their matching performance. A pioneering work on spectral matching is that by Fabian (1967), who developed an imaging system for agricultural surveying purposes. This work involved the development of x-y light-pen spectrum-matching schemes and subsequent extraction of the signatures of vegetation types of interest. This work also discussed the confusion that occurs while identifying crops of similar spectral characteristics, the occurrence of false alarms, and the need to reduce matching errors due to illumination. The availability of abundant spectral data from laboratory and field-based spectroradiometry and hyperspectral imagery has led to the development of diverse spectral databases that are utilized in varied applications. This repository of spectral signatures, called ‘spectral libraries’, further facilitates the spectral matching process. Apart from standard spectral libraries such as that of the United Stated Geological Survey (USGS), many research-oriented libraries such as those of the Jet Propulsion Laboratory (JPL), Johns Hopkins University (JHU), and the United Nations Educational Scientific and Cultural Organization (UNESCO) have been created. The need to speed up the search-and-match process using large spectral libraries has resulted in the automation of matching techniques. Automation algorithms perform well because they look for specific, spectrally defined targets (Manolakis et al. 2009). In a recent work, Parshakov (2012) stated that spectral matching techniques are well suited for automation due to their ability to map data from different sensors coupled with the reduced need for additional data about the study area. Spectral matching approaches are classified as (i) similarity match and (ii) identity match. In identity match, a match for the unknown spectra is assumed to be present in the spectral library, while in similarity match, the unknown spectra are not available in the spectral library for matching. The matching approaches are further classified as (i) deterministic and (ii) stochastic (Vishnu, Nidamanuri, and Bremananth 2013). In the deterministic type, algorithms are based on the geometrical and physical aspects of the unknown and reference spectra. These include the Euclidean Distance Measure (ED), Spectral Angle Mapper (SAM), Spectral Correlation Measure (SCM), Binary Encoding (BE), and Spectral Feature Fitting (SFF) techniques. Stochastic algorithms based on the distributions of the spectral reflectance of target pixels include Spectral Information Divergence (SID) and Constrained Energy Minimization (CEM). To accommodate the influencing factors (spectral library, absorption features, a priori knowledge, false hits) and improve performance, a phenomenal evolution of spectral matching approaches has occurred, resulting in variants of the basic algorithms. For example, in the traditional, deterministic technique – Spectral Angle Mapper (Kruse et al. 1993) – similarity is assessed based on the angle between the reference and target spectra. By including the hyper-angle measure, a variant of SAM called Modified Spectral Angle Mapper (MSAM) (Staenz et al. 1999) resulted in improved accuracy by decreasing the illumination effects. Similarly, Spectral Correlation Angle (SCA) and Spectral Gradient Angle (SGA) approaches, proposed by Robila and Gershman (2005), utilize

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the correlation coefficient and gradients in terms of the angle. The correlation coefficient, being a better descriptor of the spectra, led to better performance of SCA in comparison with SAM. Implementing the SAM measure for an optimal spectral library resulted in the Optimal Spectral Angle Mapper (Luc et al. 2005). In the Extended Spectral Angle Mapper (ESAM) proposed by Li et al. (2014), false alarms are detected using the red edge position. SAM also proved to be a good contributor when used in combination with other approaches. When combined with spectral information divergence measures such as SAM-SID (Du et al. 2004), with amplitude difference measure as Normalized Spectral Similarity Score (NS3) (Nidamanuri and Zbell 2011a) and Jeffries–Matusita distance as JM-SAM (Padma and Sanjeevi 2014), the SAM approach yielded better accuracy in matching targets. A detailed listing of several matching approaches and a discussion on their advantages and limitations is presented in Table 1. 2. Applications of spectral matching The advent of spectral matching approaches has widened the scope of hyperspectral remote sensing. Major domains such as image classification, band selection, and target detection have benefitted from these approaches. Matching techniques are used to match class spectra with the ideal spectra for several applications such as identification of crop type and plant species, and mineral exploration (Thenkabail et al. 2007). These techniques have increased the precision and accuracy of hyperspectral image classification compared with the multispectral classifiers which are affected by the Hughes phenomenon of hyperspectral data (Xie et al. 2011). Spectral matching can also assist in the selection of pure pixels (end-members) in a scene for a given target material and it also searches the spectral libraries for specific targets (Schwarz and Staenz 2001). Furthermore, end-member extraction and material abundance estimation, which are considered as the challenges of hyperspectral image processing, can be automated using spectral matching techniques. Gupta and Rajan (2010) used matching algorithms such as Euclidean distance, Dynamic Time Warping, Derivative Dynamic Time Warping, and Constrained Dynamic Time Warping to study the temporal shifts in vegetation and crops from pixel to pixel, based on geographical location. Apart from the above, spectral matching aids in oil spill detection (Andreoli et al. 2007), discriminating salt-affected soils (Farifteh, Van Der Meer, and Carranza 2007), bathymetry modelling for ocean studies (Ma et al. 2014), and lithological mapping (Zhang and Li 2014). Furthermore, spectral matching in planetary remote sensing was demonstrated by Evans (2007), who used the spectral correlation algorithm to map lunar geological features. In another study by Zhu et al. (2006), Spectral Angle Mapper and Spectral Feature Fitting algorithms were used to identify lithological units on the surface of Mars using OMEGA/Mars Express data. Cahill et al. (2010) implemented Spectral Feature Fitting and Spectral Correlation-based matching and analysed the highland and mare soils of the lunar surface using M3 imagery acquired by Chandrayaan-1. Thus, it will be seen that the number of applications and the quality of feature detection approaches using hyperspectral images have increased with the advent of several spectral matching algorithms. 3. Factors influencing the performance of spectral matching algorithms Analysis of several studies on spectral matching approaches has provided an insight into the factors that influence their performance. These approaches have evolved from simple

Advantages

Computes distance between two Computationally simple. spectra Sensitive to differences in DN. Temporal shifts in spectra are not captured Dot Product (DP) Cosine of angle between Treats intensity differences in a unknown and library spectra is continuous manner. Assigns computed maximum value when spectra are identical Least-Squares Computes least-squares distance Accurate when used with look-up Minimization (LSM) table Normalized Distance between spectra is Resistant to noise. Requires less Compression measured by a standard information on data. Distance (NCD) compressor Compatible with diverse data. Resolves similarity between unclear portions of spectra Combines continuum removed Suitable to characterize minor, Adaptive version of and continuum intact spectra major, and combined CICR (dCICR) absorption features Z-Score Distance (ZSD) Difference between reference Performs well. Accurate spectrum and class mean discrimination of similar compared to standard vegetation. Automated deviation labelling capability Mahalanobis Distance Computes statistical distance Works well for noisy spectra. (MD) between a reference spectral Employed for accurate retrieval vector and multivariate of inherent optical properties distribution of points (IOPs)

Approach

Mobley et al. (2005)

Look-up table must contain accurately calibrated spectra Depends on internal parameters of the compressor

(Continued )

Spectral vectors to be chosen Bue, Merenyi, and Csatho carefully to avoid spurious (2010) features Atmospheric correction Parshakov (2012) influences accuracy. Suitability for non- vegetation targets not assessed Computationally complex due to Gillis, Bowles, and Moses the inclusion of additional (2013) covariance matrix

Cerra et al. (2011)

Stein and Scott (1994)

Unsuitable for large libraries. Spurious peaks overlap useful peaks

Key references Gower (1985)

Limitations Complex when DN and number of bands are large

Salient features of the spectral matching approaches used in hyperspectral remote sensing.

Distance measures Euclidean Distance (ED)

Algorithm

Table 1.


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(Continued ).

Approach

Advantages

Measures both the spectral and hyper-angle between two spectra

Key references

Li et al. (2014)

(Continued )

Bertels et al. (2005)

Robila and Gershman (2005)

Robila and Gershman (2005)

Unsuitable for similar materials. Kruse et al. (1993) Intra-class variability in vegetation not accounted for (Bertels et al. 2005). Intolerant to diverse spectra (Jiao, Zhong, and Zhang 2012) Complex to compute. Sometimes Staenz et al. (1999) results in infinity value (Thenkabail et al. 2007)

Limitations

Minimizes linear effects due to the geometry of image. Suitable for data with spectral variability Spectral Correlation Correlation between reference Discriminates positive and Only linear relation between Angle (SCA) and target spectra measured in negative correlation between spectra is used. Overstated terms of angle spectra (Carvalho et al. 2000). values may lead to false alarms Insensitive to gain and offset Spectral Gradient Angle Measures spectral angle between Invariant to illumination Less effective for analysing rough (SGA) gradients of reference and spectra target spectra Optimized Spectral Spectral angle for reference Outperforms SAM measure in Highly dependent on training data Angle Mapper spectrum in optimal library is vegetation mapping. Library (OSAM) measured against target in the comprises all reference spectra library to avoid mismatch Extended Spectral SAM removes noise. SVM and Angle-based threshold and REP Applicability to multi-class Angle Mapper Vertex Component Analysis result in better detection mapping is yet to be assessed. (ESAM) used to extract pure spectral compared with other methods May not be effective when two library. REP mitigates false or more peak features present alarms at 720 nm

Modified Spectral Angle Mapper (MSAM)

Angle measures Spectral Angle Mapper Angle between two spectra Invariant to scale and (SAM) quantifies similarity/mismatch. illumination. Computationally simple and fast. Available in many image-processing packages

Algorithm

Table 1.


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(Continued ).

XOR logical operation used to assess similarity between known and unknown derivative spectra

Cross-correlation at different match positions in test and reference spectra are calculated Cross-correlation is assessed between continuum removed and reference spectra Combines continuum-removed (CR) and continuum-intact (CI) characteristics

Approach

Limitations

Fast and feasible processing

Bue, Merenyi, and Csatho (2009)

Van Der Meer (2000)

Van Der Meer and Bakker (1997)

Key references

(Continued )

Entire spectrum is not utilized for Clark, Gallagher, and matching. Noise and natural Swayze (1990) variations result in features similar to absorption

Mixed pixel is not considered. Kim (2011) Appropriate bin size and bits representation of spectral derivative required for matching A priori knowledge required to Xie et al. (2011) extract shape descriptors. Loss of radiometric information during encoding of spectra

Compares materials of different Subtle spectral differences not albedos. ‘Uniform’ noise considered. Ability to resolve component is not present in the issue of mixed pixels is not results assessed Subtle spectral differences Non-diagnostic noise features identified. Preserves the shapes enhanced with absorption of cross-correlograms features Performs better as it considers A priori knowledge required to absorption feature and label materials continuum-removed spectra

Advantages

Improvised Binary Encoding Spatial descriptors prevent (Scott 1988). Spectral and mismatch in case of similar spatial descriptors of region spectra. Improved storage of are binary coded and matched hyperspectral information. with target code using Hamming distance measure Feature-based matching measures Spectral Feature Fitting Absorption feature-based Sensitivity to unique absorption (SFF) technique that uses leastfeatures allows accurate squares technique identification. Minimizes effects of grain size variations and illumination (Warner, Nellis, and Foody 2009). Available in expert systems

New Binary Encoding Algorithm

Encoding measures Bit Map Index-Based Matching

CCSM ContinuumRemoved Algorithm (CR-CCSM) dCICR CCSM

Correlation measures Cross-Correlogram Spectral Matcher (CCSM)

Algorithm

Table 1.



(Continued ). Limitations

Key references

Detects one target source at a Sensitive to noise. Results in false Harsanyi (1993) time. Performs better than SSV alarms. Detection of small and MSAM (Homayouni and targets difficult. Less sensitive Roux 2004). to shape of spectra (Frolov and Smith 1999). Less adaptive to image complexity (Continued )

Mismatch due to spectra of Clark et al. (2003) certain materials being similar in a specific wavelength range but different in another Extra peaks due to interpolation Brown (2006) overlap real peaks. Lacks continuum modelling as only absorption features are analysed. Limited to SWIR region Only significant variations in the Kumar et al. (2014) High precision in identifying spectrum are detected for mixed pixels as it utilizes fitting. There is a potential for multi-scale salient features and inclusion of variations due to shape characteristics of the noise pixel

Advantages

Computes least-squares goodness Entire spectrum is utilized and of fit of multiple absorption multiple features are features for best matching characterized in different wavelengths A two-step automated procedure Utilizes prominent absorption involving derivative peak peaks of derivative spectra. finding and iterative leastPreserves shape of the spectra squares fitting and is suited to automated mineral expert systems

Approach

Combined VariableSpectral features detected from Interval Spectral the target spectra by VISA Average (VISA) method are matched with a method and Spectral library spectrum using SCM Curve Matching (SCM) Information measures Constrained Energy Response of target spectra is Minimization (CEM) maximized by suppressing background spectra

Spectral Curve Fitting (SCF)

Multi-Range Spectral Feature Fitting (MRSFF)

Algorithm

Table 1.



(Continued ).

Derived from the Generalized Likelihood Ratio. Uses covariance matrix to identify the background and compute the ratio

Approach

Combined measures Spectral Similarity Value (SSV)

Combines Spectral Correlation Similarity (SCS) and ED

Locally Adaptive CEM Modified CEM. Sample (LCEM) correlation matrix is designed for target spectrum and standard CEM operator is applied Spectral Information Derived from divergence Divergence (SID) information theory. Measures probability of spectral discrepancy

Adaptive Cosine Estimator (ACE)

Algorithm

Table 1. Limitations

Key references

(Continued )

Shape and distance used to assess Statistical information on spectral Granahan and Sweet (2001) similarity. Unambiguous ED bands may not be utilized resolved by shape measure. Outperforms SCS, ED, and MSAM (Thenkabail et al. 2007).

Invariant to scaling of spectra. Prior knowledge of training data Kraut and Scharf (1999) Constant false alarm on scaling required for computing (ENVI Classic Tutorial 2014) covariance matrix. Detection is Robust for target mismatch, poor in the presence of noise variability and background (Guo and Osher 2011) interference. Fits reflectance and radiance domains (Manolakis et al. 2009) Improved detection of small Loss of information during matrix Frolov and Smith (1999) targets in complex background computation. Detection is due to spectral variability difficult when target is factor. More adaptive to image dominant in proportion content Better quantification of similarity Works mainly for mixed-pixel Chang (2000) than spectral angle target spectra. Shows a (Nidamanuri and Zbell 2010). confused dendogram when High dimensional images used for a compression process accommodated without data (Cerra et al. 2011) redundancy approach (Vishnu, Nidamanuri, and Bremananth 2013)

Advantages



(Continued ).

Approach

Advantages

Limitations

Hidden Markov Model- Statistical measure of Higher discriminability than Computation is difficult for Based Divergence information on distance SAM, ED, and SID due to complicated spectrum. Results (HMMID) between two HMM-modelled characterization of unobserved in increased hidden states spectra spectral properties of a pixel Spectral Information Combines deterministic SAM Spectral discriminability is 5 and Quantitative angle measure is Divergence-Spectral and stochastic SID approaches 2 times higher than individual insufficient to complement the Angle Mapper in Tan and Sin SAM and SID, respectively. qualitative information from (SAM-SID) Utilizes band-wise spectral SID information Spectral Information Hybrid of SID and SCA SCA results in higher Sensitivity of SCA to certain Divergence-Spectral measures Tan and Sine discriminatory power of SIDwavelengths affects results. Correlation Angle versions. SCA than SID-SAM hybrid The sine version is not (SID-SCA) measure considered due to lower similarity values Combines spectral angle and Spectral variability is captured False-positives/negatives occur. Normalized Spectral amplitude difference between with high accuracy by Extensive spectral library with Similarity Score reference and target spectra including amplitude parameter standard data grouping (NS3) required for accuracy JM-SAM-based mixed Combines the stochastic Jeffries– Higher discriminability than JM Suitability to discriminate and measure Matusita distance and and SAM assess proportions within a deterministic SAM mixed pixel is yet to be assessed

Algorithm

Table 1.


Padma and Sanjeevi (2014)

Nidamanuri and Zbell (2011a)

Naresh Kumar et al. (2011)

Du et al. (2004)

Du and Chang (2001)

Key references


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cluster analysis to the advanced technique of automated matching by adapting to the following factors. 3.1. Spectral library Spectral libraries used for matching are varied by nature. In some approaches, a standard spectral library is used while in others a specialized library is developed. To exploit the efficiency of hyperspectral sensors, a well-populated spectral library is required. While there is a strong need to develop the concept of ‘exemplar spectra’ to enhance spectral matching capability (Gomez 2001), there are valid reasons for adopting a small spectral library. These would provide a reduced search space and the computational time during the process of matching the query spectrum. Studies on the development of a quality factor (Q) for spectral matching techniques state that a larger library results in lower Q-values. This is due to the likelihood of extracting different material types from the unknown in the library (Nidamanuri and Zbell 2011b). It is pertinent to mention here that integration of spectral libraries has resulted in increased accuracy of target matching and identification in several applications. Spectral library transfer in spatial (library spectra collected from many geographical locations) and temporal (library spectra collected at different instances) domains has been attempted for assessing spectral variability among crops such as alfalfa, triticale, winter barley, winter rape, winter rye, and winter wheat (Nidamanuri and Zbell 2011a). Spectral libraries in different applications are referred to as either ‘spectral signature database’ (Ruby and Fischer 2002), ‘information service’ (Leenaars 2013) or ‘look-up table’ (Mobley et al. 2005). An ideal spectral library in synchronization with the hyperspectral sensor is of great significance in matching techniques. Image-processing software has inbuilt spectral libraries developed by USGS, JHU, JPL, and NASA – the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Besides, various research works have been carried out to build extensive and specialized soil spectral libraries (the Global Soil Spectral Library) (Viscarra Rossel 2008); the Czech soil spectral library (Brodsky et al. 2011); urban features (Santa Barbara library) (Herold et al. 2004); Universiti Putra Malaysia’s spectral library (Nasarudin and Shafri 2011); vegetation species (Vegetation Spectral Library 2014) developed by Systems Ecology Laboratory at the University of Texas with National Science Foundation support; JPL’s HyspIRI Ecosystem Spectral Library (Hook 2014); wetlands of indigenous environments (webbased Poyang Lake library (Fang et al. 2007); the coastal wetlands of California, Texas, and Mississippi (Zomer, Trabucco, and Ustin 2009); and the benthic habitats library (Louchard et al. 2003). In regard to planetary research, spectral data of analogous minerals and collections from planetary missions have created libraries, with the Reflectance Experiment Laboratory (RELAB 2014), the Planetary Data System (PDS Geosciences Spectral Library 2014), Arizona State University’s Thermal Emission Laboratory (Christensen et al. 2000), and a planetary component of USGS library being typical examples. Furthermore, spectral libraries for analysis of oil spills, phenological crop changes, and many more applications are also available. With several studies carried out on spectral databases, the need to publish and share their components led to the development of online spectral libraries, where an interactive user interface is available to view spectral plots. Functionalities for querying and downloading are also available. SPECCHIO (Bojinski et al. 2003) is one such example, having a comprehensive collection from varied spectral library projects.


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3.1.1. Construction of spectral libraries Construction of a spectral library involves the extraction and preprocessing of spectral signatures, the creation of metadata files for each signature, and storage of the collected signatures. The process of collecting and processing spectra varies depending on their source (spectroradiometry, hyperspectral images, synthetic measurements, compiled libraries, and open-source spectrometry). Spectroradiometry comprises laboratory- or field-based measurements of samples of interest. Preparation of samples and removal of instrument noise result in accurate spectral plots. Clark et al. (2007) used four types of spectroradiometer to measure samples of varying grain size under laboratory conditions for USGS. Herold et al. (2004) used an Analytical Spectral Devices (ASD) spectroradiometer to collect 4500 spectral plots of urban features. Such abundant hyperspectral data have the potential to contribute to a library within a short duration. Janja (2012) states that building a spectral library using image spectra avoids the problem of atmospheric errors, which is dependent on the image and a sensor, and also requires an operator to select the appropriate spectra. Shwetank, Jain, and Bhatia (2011) developed a digital spectral library for five rice varieties cultivated in and around Bapauli town in the state of Haryana in India using EO-1 Hyperion images. In this study, a chain of preprocessing methods such as radiometric correction, geometric correction, selection of appropriate bands, detection and correction of abnormal pixels, spectral smoothing, and atmospheric correction are performed on the image before the retrieval of reflectance spectra for the library. In another study, a mineralogical spectral library for the Nili Fossae region on Mars was constructed using OMEGA/Mars Express data (Daswani 2011). Here, the author suggests that although OMEGA data hold good for angle measurement and study of spectral characteristics of large areas, they are affected by noise. The author further states that Compact Reconnaissance Imaging Spectrometer (CRISM) data are better suited to correlation-based similarity measures for sites on Mars. Recent examples of libraries for specific sensors include the efforts of JPL to construct a fully fledged ecosystem spectral library aiming to support users of the Hyperspectral Infrared Imager (HyspIRI). This queryable database, based on the template of the ASTER library, allows users to contribute their spectral data related to ecological studies. Similarly, several research proposals convey the potential of building an extensive spectral library for benthic habitats using the HICO mission. Some libraries comprise spectral measurements from both spectroradiometry and hyperspectral images. The urban spectral library developed by Herold et al. (2004) includes spectral data measured using the ASD spectroradiometer and from AVIRIS data. Apart from these sources, synthetic or simulated spectra are also used to populate libraries. A synthetic spectrum is formed by mixing certain proportions of the pure components obtained from a spectral library. Louchard et al. (2003) constructed a spectral library for benthic habitats with simulated spectra created using measured values of bottom reflectance and water-inherent optical properties. Several newly developed standard spectral libraries themselves developed into a source for developing a new spectral library. The ASTER spectral library is a compilation of the USGS, JPL, and JHU libraries (Baldridge et al. 2009). Another example is the Topographic Engineering Center (TEC) library (Ruby and Fischer 2002), which was compiled as a web-based library from various projects and their related metadata. The collected spectral plots are stored within a spectral library using various approaches based on their accessibility for users. In regard to the USGS, ASTER, and RELAB libraries, each spectrum file is stored on the website as an ASCII file entry along


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with its metadata. Furthermore, specialized tools in the image-processing and mineral identification software are available for storing the spectral data as libraries in the native format. Here, the user provides each spectrum file or pixel location in the hyperspectral image from which the spectra need to be extracted as an input for generation of the library. An advanced form of this concept led to the construction of an online database (SPECCHIO), where spectral data and related ancillary information are stored within a database system based on the entity–relationship model. This type of database model contributes to open-source spectrometry, where data from varied resources are shared free of cost and stored at a single location for access. In addition to online spectral resources, currently portable versions of hand-held spectroradiometers are designed with user-friendly interfaces where the spectral plots collected during field visits are stored within the instrument. Another increasingly popular trend is the use of portable and cost-effective DIY spectrometer kits integrated with Spectral Workbench (2014) software, which allows fast and easy collection of spectra at any location (Public Lab Store, 2014). An overview of the characteristics of various spectral libraries available online, along with a detailed listing on their construction, components, metadata, and accessibility, are presented in Tables 2 and 3. 3.1.2. Technical requirements for construction of libraries One of the fundamental requirements for constructing a spectral library is the availability of appropriate tools for accurate processing of the collected spectra. The need for better storage, querying, and accessibility arises while creating an extensive web-based library. In the case of online or open-source spectral libraries, users are allowed to upload and share their projects. Gomez (2001) discussed the process of obtaining spectral data from various sources in a standard format as one of the characteristics of an ideal library. In SPECCHIO (Bojinski et al. 2003), the user needs to complete an inbuilt form and upload the spectral collections in either ENVI or ASCII format. This inbuilt form is a template for collecting ancillary information on sensor name, type, date of collection, etc. A database system capable of storing extensive data with integrity is required for all these transactions. Library searching or querying led to the use of advanced database management systems such as Postgre SQL. The spectral library constructed at the Planetary Emissivity Laboratory (PEL) of the German Aerospace Center employs the Postgre SQL database for storing and analysing 3 million spectra collected through the MESSENGER mission. It takes about 700 milliseconds for a simple query on this highvolume database and any range of keywords can be used for accessing relevant spectra. Furthermore, a data pipeline is set in this mission to transfer the collected data to PEL in the planetary data system format (DLR – German Aerospace Centre 2014). Such a database is integrated with the Common Gateway Interface (CGI) to enable online access for users (Bojinski et al. 2003). Besides command-based interfaces, userinteractive tools for visualization are designed to assist in searching the library. Some libraries can be made available in stand-alone and web-based forms. In the stand-alone version, plug-ins related to MATLAB and Post-GIS are designed to directly connect and visualize the library in the corresponding software. In this context, it is also pertinent to note that the library architecture plays a major role in the accuracy of the matching process. Commenting on the limitation of sequential library architecture while matching the closely similar crop spectra, Nidamanuri and Zbell (2011a) state that the numerical significance given to each candidate spectrum in the library through sequential architecture will avoid the prospect of identifying subtle differences among the crop spectra.

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VSL

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Planetary materials

Planetary materials

Earth/planetary materials Materials of various projects Planetary materials

Earth/planetary materials Urban materials

Nature of samples

*

>2000

~2463

*

>10,000

~2300

~4500

~1300

Number of spectra (approximate)

Notes: Web-based archive – spectrum and related metadata are archived in web interface as ASCII files. *Details of approximate numbers not known.

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Santa Barbara ASTER

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Airborne Spaceborne Laboratory sensors sensors Compilation

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Web-based archive Web-based archive Web-based archive Postgre SQL

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p

p

p

p

p

p

p

p

p

p

p

p

p

p

p

–

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–

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User Special Integration interface software with Database for viewing Metadata Convoluted Querying for other Authorization Downloading Upgrading Uploading type spectra availability data facility analysis software required facility facility option

Overview of the salient features of spectral libraries used in remote sensing.

Spectral libraryz

Table 2.



Components of the library

Metadata availability

Accessibility

(Continued )

Spectra measured using: Beckman Current version is splib06a with Documentation is about origin, Spectral plots can be downloaded 5270, ASD, Nicolet Fourier 1300 spectra of minerals, rocks, sample purity, and related as ASCII files. Older versions Transform Infrared, and soils and mixtures, coatings, details. Digital photographs of (splib05 and splib04) are also NASA’s AVIRIS radiometers. liquids, liquid mixtures, certain samples available. provided with convolved Mineral analysis done by XRD, volatiles, frozen volatiles, manSPECtrum Processing Routines libraries that are embedded in electron microprobe (EM), made chemicals, plants, (Specpr) (Clark et al. 1993) several image-processing XRF, and petrography. For vegetation mixtures and related convolves USGS library spectra packages for spectral analysis vegetation, documentation microorganism. and the moon relates to location and species and other planets. Next version type. It took 20 years to (splib07) is under way construct (Clark et al. 2007) Santa Barbara Four categories of spectra are 4500 spectra representing 108 Digital photographs for some Interactive map displaying Urban listed. Each spectrum is unique urban targets (roofs/ urban targets. Location Fairview and Cathedral Oak Spectral measured (at 350–2400 nm) for buildings, transportation coordinates, class names, and sites and their targets of Asphalt Library discriminatory ability using the surfaces, and non-built-up spectra name are present road. Spreadsheet for targets Bhattacharya distance (Herold surfaces). Spectra convolved to available for downloading. et al. 2004). AVIRIS wavelength ranges AVIRIS convolved library and ASD spectra of roofs, transportation, and non-built up surfaces can be accessed ASTER Spectral Compiled from USGS, JHU, and The current version (2.0) contains Each spectrum is supplied with Latest version (2.0) can be Library JPL libraries. Contributions 2300 spectra (at 0.4–15.4 µm) details of type, class, particle downloaded or obtained as CDwere converted to a common of rocks, minerals, snow, ice, size, sample number, owner, ROM. The website contains an standard and ancillary data are terrestrial soil, meteorites, and wavelength range, origin, and interactive search tool to query included. Spectra obtained by vegetation. JPL libraries are measurement type. and view the spectra of different Beckman, Perkin, Perknic, and updated regularly targets Nicolet radiometers (Baldridge et al. 2009).

USGS Digital Spectral Library

Review of construction

Salient features of the spectral libraries used in remote sensing.

Spectral library

Table 3.



Reflectance Experiment Laboratory (RELAB)

SPECCHIO database




Accessibility

(Continued )

A comprehensive database of Current version (V 3.1) contains Each spectrum is provided with An authorized user can access and spectra from USGS, JHU, JPL, 60 field campaign data (111,202 the ancillary details classified as download the database USDA Beltsville, field spectra of various targets). general, position, model, sensor, application and install in the campaign, limnology and ~4802 contributions are from target type, and land-use type. local system. Online version vegetation studies, and spectral libraries. Library is Tertiary information for each of can also be used to store and parametric models. A data constantly updated. these categories is available retrieve required data model is designed for effective organization of information in an entity–relationship framework. Users can contribute in either ENVI or ASCII format with ancillary data (Bojinski et al. 2003). The database works by MySQL and JAVA applications. Plug-ins for Arc GIS and MATLAB available for querying, viewing, and input of spectra and metadata. Spectra obtained from Database of Version 2006a Standard deviation measures are Spectral plots can be downloaded bidirectional and FTIR comprises spectra related to provided along with the spectral in compressed format from the spectroradiometer. Request for projects that include planetary plot. Ancillary data are website. Spectral files are in spectral measurement and samples and their terrestrial available for each spectrum file both ASCII and text format. inclusion in the database can be analogues Users can send their samples to sent along with the sample. RELAB for obtaining spectral Ancillary data about sample measurement and subsequent name, origin, particle size, uploading into the library. texture, location, target type, and subtype are to be submitted

(Continued ).

Spectral library

Table 3.



(Continued ).




Accessibility

Planetary Data Built by incorporating the Library contains measurements Metadata on product ID, spectral Web interface provides tools for System (PDS) Compact Reconnaissance related to earth, lunar, and range, provider ID, search, display, and Geosciences Imaging Spectrometer (CRISM) meteorite materials. Spectra of measurement geometry, downloading. Search is based Spectral spectral library and data from 1228 specimens and 2463 instrument used, azimuth, on keywords, specimen Library other projects. CRISM library products are present in the incidence, emission, and phase location, texture, composition, comprises 2260 spectra from current archive angle are provided and supplier information. 1134 analogue materials from Interactive graph plot can be Mars. Users can contribute viewed. Spectral plots are spectra by submitting a available. CRISM library can be proposal downloaded Arizona State Spectral measurements of handSpectra are archived in the current Ancillary data on sample Users can access, search, University processed pure analogues of version (v 1.1) of ASU library composition and quality, download, and upload the (ASU) Martian rocks (710–1000 nm) categorized as general info, spectral data. Search filters are Thermal at 5–45 µm are obtained using spectral info, microprobe based on library albums, sample Emission Mattson Cygnus-100 analysis, bulk analysis, XRD name, type, subgroup, and Laboratory interferometric spectrometer analysis, and modal mineralogy quality. Spectral plots can be (Christensen et al. 2000). Users are available at the web viewed and exported in can upload their spectral data interface different file formats into the library with metadata Vegetation Built using spectra of vegetation Also comprises an extensive Metadata on file name, location, Web interface allows for search Spectral and land cover collected from collection of spectra of mixed land-cover type, weather, date based on metadata and Library different research projects. vegetation and time, instrument, and downloading of the spectra (VSL) PostgreSQL database is used for sensor are provided. Digital files. Users can contribute their storage, query, and retrieval of photographs of samples are spectra collections in ASCII data appended format

Spectral library

Table 3.





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Listing the limitations of the database models, Rasaiah et al. (2011) mention that, as in the case of SPECCHIO (Bojinski et al. 2003) and Hyperspectral.info (Ferwerda, Jones, and Du 2006), these models have no mechanisms for tracking updates and transactions within the database. Furthermore, there is no protocol for assuring data quality. The authors also cite the static nature of the data-storing capacity in the ASTER and USGS spectral libraries to be a limitation, despite their comprehensive structure. These limitations, as cited by the authors, were overcome in a data warehousing model which has a unique mechanism for tracking updates, thereby preventing data corruption and flagging the metadata with their respective quality measures. Further, the authors confirmed the efficiency of data warehousing models to store spectral libraries and hyperspectral databases from several sources in the form of a centralized repository. In another study, the authors (Rasaiah et al. 2012) state that metadata are a central component in such models and suggest certain standardized, in situ metadata collection-and-documentation procedures that can facilitate data exchanges. With the development of extensive libraries, the inclusion of different and current sensor systems has to be ensured. Spectra derived using hand-held spectroradiometry and hyperspectral images differ in terms of wavelength ranges. To solve the issue of compatibility, spectral libraries from the USGS provide convolved spectra for different sensors. 3.2. Spectral features The spectrum of a material is characterized by the presence of absorption features which are exploited for accurate matching. Stein and Scott (1994) proposed the probability-based matching (PBM) method for a mass spectral database. This technique is based on the peaks in the target spectrum and in the library spectrum falling within a predefined abundance window. Here, the non-matching peaks in the target spectrum are treated as impurities and are ‘flagged’. Another algorithm used by the authors is the Dot Product measure, which continuously compares the peak intensity differences in the library and reference spectra and yields maximum value in the case of a bad match. Furthermore, the authors improved the performance of the Dot Product algorithm by adding weights to the relative intensities for spectra with many common peaks. The limitation of this approach is that, in some instances, absorption peaks are confused with spurious peaks. The flat response regions of a spectrum with added noise take on the appearance of small peaks. The application of the de-noising techniques might eliminate the useful absorption peaks. Spectral library search tools such as QuickMod employ spectrum–spectrum matching and scoring algorithms, where a weight is given to the intensities of matching and nonmatching peaks (Ahrne et al. 2011). The small informative peaks and overlapped peaks in the spectra cannot be easily identified by the software and hence the shape of the peak cannot be described accurately. To overcome this difficulty, point-to-point matching algorithms were developed where all the points in the spectra are used (Li et al. 2006). Lau, Hon, and Bai (2000) developed the Effective Peak Matching technique where the positions of three peaks in the sample spectrum are compared to the three largest peaks in the selected reference spectrum. All reference spectra with matching peaks are selected. The complexity of a spectrum increases with the presence of many peaks of various size. Stein and Scott (1994) attributed multiple major and minor peaks to such a complexity. The authors realized the limitation of the Dot Product algorithm in handling small spectral peaks. Continuous improvisations have been made in eliminating spurious peak effects and identifying real peaks (Levin 1999; Hansen and Smedsgaard 2004; Coombes et al. 2005). Clark, Gallagher, and Swayze (1990) proposed the Spectral Feature Fitting


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(SFF) algorithm, which utilizes the inherent absorption patterns between two spectra for matching. This technique was modified as Multi-Range Spectral Feature Fitting (MRSFF) (Clark et al. 2003), where the absorption features at various wavelength ranges are considered for matching. However, similar materials may not always have matching spectra at different ranges of wavelength. To overcome this issue, Pan, Huang, and Wang (2013) devised a variance–covariance weight method for MRSFF. Here, differential weights are assigned to the wavelength ranges of the spectrum based on their importance for matching. Kumar et al. (2006) introduced the Variable Interval Spectral Average (VISA) method, where variance is computed for each region in the spectrum. When the variance exceeds a threshold, the presence of a spike is confirmed. The position and line width of these features are stored and used as a reference for matching. In this approach, an appropriate threshold needs to be used to avoid noise features, which can also result in high variance like that of the absorption troughs. Hence it could be said that starting with the spectral feature fitting technique (Clark, Gallagher, and Swayze 1990), several algorithms that utilize the patterns of the peaks and troughs have evolved.

3.3. Illumination effects Illumination-independent spectral signatures for matching are obtained after radiometric correction. In some cases, mismatch occurs when two spectra of similar materials originate from surfaces of different orientation. Hence, a measure for the likelihood of their equality has to be applied through a threshold, for which a precise knowledge of the surface orientation and a temporal comparison of identical objects illuminated from differing sun angles is required (Wiemker and Hepp 1994). The Spectral Angle Mapper (Kruse et al. 1993) and Spectral Gradient Angle (Robila and Gershman 2005) algorithms are insensitive to illumination differences in the spectra. Illumination effects can be mitigated by scaling each signature in the library by its Euclidean norm. Such a scaling will discard the geometric albedo in the signatures, but preserve the spectral angles (Bue, Merenyi, and Csatho 2010). Nidamanuri and Zbell (2011a) collected spectral measurements only during the hours 11.00–13.00 to minimize the effects of illumination. In another study, Schiefer, Hostert, and Damm (2005) analysed the effects of view angle of hyperspectral data for urban areas, and stated that illumination effects severely influence the spectral information.

3.4. A priori knowledge In a few cases of spectral matching, a priori knowledge of background spectral information increases the accuracy of spectral matching. The adaptive coherence estimator (ACE), also known as the Adaptive Subspace Detector, which is an extension of the generalized likelihood ratio test (GLRT), employs a covariance matrix to identify the background information (Kraut and Scharf 1999). In contrast, the template-matching approach involving l1 minimization requires neither background information nor the assumption of multivariate normal distribution of the background during computation. The l1 minimization algorithm, however, depends only on the spectrum of the material of interest, which in turn is dependent on the end-member extraction process (Guo and Osher 2011). Other algorithms that use more information from the reference spectrum include Mean Squared Error Statistics (MSES) (Staenz, Schwarz, and Cheriyan 1996), which uses per-band


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noise, and the Cross-Correlogram Spectral Matcher (CCSM) (Van Der Meer and Bakker 1997), which provides more than a single-measure spectral similarity. Xie et al. (2011) suggested an improvised version of the binary encoding algorithm that uses shape descriptors such as area, asymmetry, elliptic fit, rectangular fit, ratio of length-to-width, and compactness of the spectra for matching. Thus, it could be inferred that the integration of a priori knowledge during spectral matching can reduce the occurrence of false hits and enhance algorithm performance.

3.5. False hits False hits are defined as poor matches of target and library spectra, which are assigned a high matching score. The top-scoring library spectrum is labelled as the perfect match. In spectral searching, the top-score matches that have turned out to be a mismatch are termed false-positives. This refers to the number of non-target pixels incorrectly classified as the target (Homayouni and Roux 2004). A target spectrum may be matched with the reference spectrum of a different material due to (i) the scoring function assigning a reasonable score to a poor spectral match and (ii) a low-quality library spectrum. A low-quality library includes noisy and contaminated query spectra. In a study of point-to-point pattern matching, Li et al. (2006) stated that even a correlation coefficient of r = 0.99 does not mean a match in all situations. It is the analyst’s responsibility to decide whether the spectral match corresponds to the real situation. While comparing the mismatch and realtime spectra, Manolakis et al. (2009) concluded that in practice, the target signature is either imperfectly measured leading to mismatch or it exhibits spectral variability. Robust matched filter algorithms use covariance regularization to address this problem of mismatch due to spectral variability. While studying citrus greening disease at the Citrus Research and Education Center (CREC), Central Florida, USA, using airborne hyperspectral images, Li et al. (2012) set the red edge position (REP) at 720 nm and filtered the false-positive pixels from the result of SAM-based matching. The pixel identified as ‘healthy’ by SAM, but with REP below 720 nm, is categorized as false-positive and filtered. This approach may not work well in some instances because in continuous spectra there may be a double-peak feature near 700 and 725 nm causing a disruption in relating the REP factor to the health status of the crop. This conclusion is similar to that of Clevers, Kooistra, and Salas (2004). In such a case, the second derivative spectra will be required for accurate extraction of the REP factor. Though many causes for mismatch have been listed above and reviewed, the study of other types of mismatch needs a full investigation.

3.6. Distortion of spectra due to environmental factors Hyperspectral data, derived either from hand-held spectroradiometer or hyperspectral images, could be distorted due to several environmental factors, thus resulting in mismatch. An example of such a distortion is the vegetation index profile with different growing practices (sowing, senescence, harvest, etc.) for two similar vegetation classes which appear as points separated by large distances. This results in labelling same species as different classes. Modification-tolerant tools are being developed where, even if part of the target spectrum is found, the result might be a number of partially matched hits (Li et al. 2004; Ahrne et al. 2011). Such instances of spectral matching with environmentally modified spectra were reported during an oil spill analysis (Salem, El-Ghazawi, and

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Kafatos 2001; Li et al. 2004), and during quality control of herbal medicines (Chau et al. 2001). 3.7. Threshold requirement Thresholding is an important step in spectral matching, used for selecting the ranges in the spectra, identifying false alarms, and flagging peaks. Despite the availability of competitive techniques of selecting a threshold, it still remains a difficult task because an optimal threshold has to maintain a low number of false alarms and a high number of correct decisions. Hence there is always a compromise in choosing a low threshold to increase the probability of detection and a high threshold to decrease false alarm rates. (Manolakis, Marden, and Shaw 2003). According to Schwarz and Staenz (2001), the adaptive threshold technique, which is based on the training area statistics, can be used in combination with spectral matching techniques to classify the spectra. This technique was demonstrated using the Modified Spectral Angle Mapper (MSAM) on simulated data and Hyperspectral CASI imagery collected over an agricultural site in southern Manitoba, Canada. In the case of the binary coding technique, simple segmentation of the spectrum into a set of uniformly sized sub-ranges can give 100% separation. The method of choosing multiple thresholds in an improvised binary coding consists of determining the mean brightness of a pixel vector and setting the upper and lower thresholds (Jia and Richards 1993). Li et al. (2012) arrived at a threshold value based on a trial-and-error process. In this study on mapping disease-prone citrus, the threshold value that resulted in high detection accuracy from one of the three values (0.05, 0.1, and 0.15) was used. Galal, Hassan, and Imam (2012) introduced the concept of ‘learnable hyperspectral measures’ to overcome the limitations of using a static threshold in assessing similarity or dissimilarity between two spectra. In this study, the authors consolidated the statistics obtained from nine matching measures for creating the combined similarity and dissimilarity pattern, which was then classified using the Support Vector Machine (SVM). The classifier component provides the required adaptive similarity threshold, resulting in precise material identification. Hence, proper setting of the threshold can help immensely in improving the performance of spectral matching approaches since these control interference by spurious peaks, false alarms, and mixed pixels. 3.8. Mixed pixels With a diverse range of targets and backgrounds, matching approaches have to be equipped in tackling the mixed pixels in an image. Although a reasonable level of identification has been achieved for pure target spectra, the identification level for target mixtures is yet to be addressed (Vishnu, Nidamanuri, and Bremananth 2013). Several matching approaches were insensitive to this aspect. In one study on template matching, the l1 minimization-based approach was developed to work on an inhomogeneous background. Here, the algorithm obtains the correct matching pixels if the pure material’s contribution to the mixed pixel is higher (Guo and Osher 2011). The Hidden Markov Model Information Divergence (HMMID) method, which is a statistical approach, provides enhanced capability in capturing spectral variability and is effective in identifying mixed spectra (Du and Chang 2001). Manolakis et al. (2009), in a work on identifying the best hyperspectral detection algorithm, explained the need for spectral matching approaches to detect subpixel targets efficiently. In this work, the target is modelled and



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identified based on statistics such as mean, variance, and covariance, which are calculated from the background information. In the case of the availability of a spectral library, all the candidates are used to create a low-dimensional subspace or background for identifying the subpixel target. However, in this approach, a detailed knowledge of the background and proper selection of threshold is required for accurate matching. The traditional linear unmixing algorithm, which decomposes the subpixel into its components based on the Euclidean distance measure, is sensitive to the magnitude of the spectrum. To overcome this issue, Chen et al. (2009) in their study on mapping biological soil crusts in Gurbantunggut Desert, China, proposed a subpixel framework integrating spectral matching measures such as SAM, SCM, and SID to identify the best match for the unknown spectrum to a weighted sum of end-member spectra. This technique, based on the Sequential Quadratic Programming (SQP) method, provided improved identification of mixed pixels compared with the existing unmixing approaches. However, it should be noted that a well-built spectral library with the required mixture components is necessary for such improved performance. 4. Improvised and combined algorithms The limitations of spectral matching techniques can be overcome by certain modifications to the existing version. For example, although the SAM averages out the absorption features required for efficient discrimination, it is unsuitable for discriminating closely related materials. Besides, it cannot distinguish the negative and positive correlation between the target and the reference. Staenz et al. (1999) developed the MSAM, which combines both the shape and magnitude of spectra. It thus overcomes the limitation of SAM, which uses only the shape feature. Similar improvisations led to the development of algorithms such as the Normalized Euclidean Distance (NED) (Robila and Gershman 2005) and Cross-Correlogram Spectral Matcher–Continuum Removed (CCSM-CR) (Van Der Meer 2000). The common factor in both algorithms is that matching performance is increased by modifying the nature of the input spectrum. While NED deals with normalized spectra, the CCSM-CR approach makes evident the role of the continuum removal method as a precursor for matching. The most effective method of improving the performance of spectral matching involves the combination of two or more qualitative measures with the qualitative measures of matching. According to Vishnu, Nidamanuri, and Bremananth (2013), a combination of methods results in increased accuracy. Examples of combining two matching measures include Spectral Similarity Value (Homayouni and Roux 2004; Granahan and Sweet 2001), Spectral Information Divergence–Spectral Angle Mapper (SAM-SID mixed measure) (Du et al. 2004), Spectral Information Divergence–Spectral correlation angle (SID-SCA mixed measure) (Naresh Kumar et al. 2011), Normalized Spectral Similarity Score (NS3) (Nidamanuri and Zbell 2011a), and Jeffries–MatusitaSpectral Angle Mapper (JM-SAM) (Padma and Sanjeevi 2014). Homayouni and Roux (2004) proposed the fusion of Spectral Similarity Value (SSV), Constrained Energy Minimization (CEM), and MSAM for precise matching. Here the authors confirmed that although primary algorithms are important in identifying pure pixels, the hybrid techniques have considerable scope to increase the accuracy of the matching process. Similarly, while identifying land-use classes, Thenkabail et al. (2007) concluded that the SSV technique, which captures both the distance and shape measure of the spectra, is better than the Euclidean Distance, Modified Spectral Angle Similarity, and Spectral Correlation Similarity measures. It should be noted that the combined measures (SID-SAM, SID-SCA,

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and NS3) handled intra-species and inter-species variability more effectively than the individual algorithms. For instance, Dudeni and Debba (2009) described the improved matching ability of SID-SAM in characterizing seven spectrally similar African savannah tree species. Naresh Kumar et al. (2011) reported that the hybrid SID-SCA measure yielded an increased matching accuracy in discriminating five varieties of Vigna species. Similarly, Nidamanuri and Zbell (2011a) indicated the complementary nature of spectral amplitude and shape measures in NS3 for efficient classification of six crop varieties collected at different phenological stages. Hence, a combination of quantitative and qualitative spectral matching techniques helps to improve accuracy and strengthen results compared with those obtained using individual techniques. 5. Performance measurement The efficiency of spectral matching techniques is assessed by various measures of performance. The Relative Spectral Discriminability Probability (RSDPB) (Dudeni and Debba 2009) computes the likelihood that a spectrum will be identified by a selective set of spectral signatures. The higher the RSDPB value, the more likely the spectra will be discriminated from others in that region of the electromagnetic spectrum. In some instances, the performance of a new spectral matching approach is compared to and evaluated against a standard approach such as the Spectral Angle Mapper. Singh, Ramakrishnan, and Mansinha (2012) evaluated matching of the target and modelled spectrum based on root mean squared error and matching scores. Qualitative approaches such as knowledge in judging the spectral identification also exist (Arora et al. 2013). Bue, Merenyi, and Csatho (2009) devised the concept of Visual Score (VS) for assessing the discriminatory capacity of matching techniques. The authors inspected the matching trends manually and assigned a score for each method. Apart from these, statistical methods such as Probability of Spectral Discrimination (PSD) (Chang 2003) and the Power of Spectral Discrimination (PWSD) (Van Der Meer 2006) have been used to assess the reliability of spectral library searches in geological material mapping. PSD measures the probability that a spectral library or its subset will be able to identify an unknown spectrum relative to other spectra in the library. In contrast, PWSD allows the calculation of spectral confusion between the target and reference spectra by using a set of spectral similarity estimates. According to Nidamanuri and Zbell (2011b), both PSD and PWSD indicate the quality of a spectral library and the search results. The authors proposed the Q-factor, which functionally performs a similar role by assessing the reliability of spectral identification. Besides, it quantifies the performance of the spectral library search method relative to the nature and type of the unknown materials and composition of the spectral library. Another accuracy assessment method, by Manolakis et al. (2009), which utilizes the Receiver Operating Characteristic (ROC) curves, plots the probability of detection versus the probability of false alarm. The ROC estimation depends on the number of available target and background pixels. Since the number of target pixels is often limited, the ROC method should be used in empirical detection evaluation with extreme care. The lack of widely accessible data from well-designed algorithms with accurate ground truth makes the experimental evaluation and comparison of algorithms a difficult process. In some cases, the implementation of the matching technique is tested using simulated spectra (Guo and Osher 2011). This alone may not be helpful for establishing the superiority of the technique used. Hence it is necessary to use a good performance measure that identifies the influence of the quality of the spectral library, false alarms, and intrusion of background information over the accuracy of matching technique.


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6. Spectral matching tools Development of interactive interfaces for visualizing spectral analysis has evolved with the emergence of several specialized spectral libraries and matching approaches. Imaging systems that emerged in this context during the early days include the Spectral Image Processing System (SIPS) (Kruse et al. 1993), Imaging Spectrometer Data Analysis System (ISDAS) developed by the Canadian Centre for Remote Sensing in 1995, and USGS’s Tricorder (Clark, Gallagher, and Swayze 1990). These systems have the basic functionalities for data input/output, interactive visualization, and analysis of imaging spectrometer data. A single algorithm such as that of SAM in SIPS, SFF in Tricorder, or MRSFF in Tetracorder govern spectral matching for applications mostly related to mineral mapping. Subsequently, based on these algorithms, an automated rule-based identification tool called the Spectral Expert System was integrated with ENVI in 2007 as a plug-in. This expert system also works based on the principle of matching absorption features and hence has modules for continuum removal. This system worked for three kinds of input: single-target spectrum, entire spectral library, and hyperspectral image. All these developments led to automation of several matching techniques and enabled their incorporation into image processing software and mineral identification tools. A brief look at the automated spectrum-matching tools characterizes the applications of these techniques to various domains, including biotechnology. The analysis of peptides, which are the chains of amino acids linked by chemical bonds, is of great significance in biotechnology. Though peptide and hyperspectral image spectra vary considerably, the continuous development of automated tools of spectrum matching for peptide identification serves as a reference for hyperspectral image processing systems. Lam et al. (2007), in their work on the development of a spectral library search method for peptide identification, stated that the SpectraST tool performed better than the SEQUEST tool. SpectraST is based on spectral searching while SEQUEST is based on sequence searching. SEQUEST developed by the Yates laboratory operates on the principle of crosscorrelation for assessing spectral similarity. In the case of satellite image processing software, ENVI, ERDAS, and PCI Geomatica include spectral analyst tools for hyperspectral data analysis. A typical example of an automated target detection tool, available as the Material Identification tool of Tactical Hyperspectral Operations Resource (THOR) workflow in ENVI version 5.1, is shown in Figure 1. The THOR workflow’s user interface and its matching capability are depicted in Figure 2, where the unknown target spectrum chosen from the AVIRIS image is matched with an appropriate candidate spectrum from the USGS library. Similarly, the Spectral Analysis Workstation in ERDAS Imagine version 8.6 has routines for automated target identification and material mapping. An overview of the capabilities of the spectral matching modules in these software programs and other stand-alone tools is presented in Table 4. Furthermore, a detailed note on their structure, operating principle, and function is given in Table 5. It can thus be inferred that there is an increasing trend in the development of userfriendly interfaces for spectral matching and their subsequent influence on material mapping. It is, however, important to analyse the integration of various specialized spectral libraries and the convolved multispectral and hyperspectral sensor-derived spectra for varied environmental and resource-mapping applications. A recent tool is the advanced mass spectral database in the form of a cloud computing. Cloud computing is a large networked environment of shared software, databases, and other computing resources from a variety of architectures. An example is the ‘mzCloud’ (Mistrik et al. 2013), which comprises a web-based interface with a large collection of libraries,


Figure 1. Illustration of automated spectral identification using the Tactical Hyperspectral Operations Resource (THOR) workflows in Exelis Visual Information Solutions 5.1 V (ENVI 5.1). The best match for the unknown spectra is assessed by the SAM approach. 1.10 Target spectrum X:538 Y:536 Library spectrum alunite3.spc

1.05 Reflectance (×100) %


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1.00 0.95 0.90 0.85 0.80 0.75 2.0

Figure 2.

2.1

2.2 2.3 Wavelength (µm)

2.4

2.5

Target and library spectrum of alunite matched using material identification tool.

appropriate metadata, and an automated target identification algorithm. One of the key features is that even if a target spectrum is not represented as a reference candidate in a library, this cloud-based community tends to identify the structural information of such input by relating several libraries. Though Rasaiah et al. (2011) opined that the lack of

p

–

– –

p p

– – p

Separate modules available for matching

p

p p p p

p p p

Graphical user interface

–

– – –

p

p p p

Commandbased operation

–

– –

p p

– p p

Preprocessing tools

–

– – –

p

–

– p

Continuum removal

Multiple* Multiple* Aux Match Feature Search Weighted Score

SAM MRSFF SAMMSAM

Matching approach

p

p p p p

– – –

Automated target detection tools

p

p p p p

p p p

Inbuilt spectral libraries

Notes: M, Mineral Mapping; T/P, Terrestrial/Planetary Applications; C, Classification. Multiple*, Comprises several matching approaches (a detailed list is shown in Table 3). Multiple**, Tool used for various applications ranging from mineral mapping, classification, end-member selection, target detection, etc.).

DARWIN

– – p p

–

p p

Stand-alone software only for matching

Overview of the salient features of the spectral matching tools.

SIPS Tetracorder PCI Geomatica ENVI ERDAS IMAGINE TSG SPECMN

Software

Table 4.


p

p p p p

– – p

Userdefined spectral libraries

p

p p p p

p p p

Spectral saving capability

p

p p p p

– – p

Creation of spectral library

p

– – p p

– – –

M

Multiple** Multiple** M M

M T/P C

Querying capability Application


Operating principle and functions

Remarks

Tetracorder

Functionalities such as Ratio spectra, Modified least-squares spectral feature fitting, Continuum removal, Constraint and identification analysis are available as commands

(Continued )

Preprocessing by Continuum removal Improvised version of ‘Tricorder’ for function. MRSFF (shape matching terrestrial and planetary applications algorithm) used to identify a target. (Clark et al. 2003). Source code and Assessment of similarity of the target to commands available in USGS website. all candidate spectra is the key feature. Highly dependent on the spectral After identification, ‘Grouping’ function libraries (USGS, JPL, and others) categorizes the targets Spectra Handling module The ‘Local Analysis’ module comprises the For SAM, the output is a raster. DN of Except SAM (hyperspectral classification in PCI Geomatica SAM and Spectra Handling tools. pixels corresponds to the angle between tool), modules exclusive for spectral ‘Spectral plot’ tool is available for the reference and target spectra. ‘Spectra matching are absent. 29 libraries viewing the spectra Handling’ tool allows the user to derive, available in version 9.1 including USGS convolve, and re-format spectra using and ASTER. Metadata can be included I2SP, SPCONVP, and SP2SP functions. for each spectrum using XML files ‘Spectral plot’ imports spectra from image or spectral library and saves into XML or SPL library Standard libraries include ASTER (2443), Spectral Analyst and ‘Mapping Methods’, ‘Spectral Analyst’, Continuum removal and spectral feature IGCP264 (139), USGS (1994), and Tactical Hyperspectral ‘Spectral Library Builder’ are available fitting possible. Target spectrum can be Operations Resource in the Spectral tools of ENVI (5.1). matched with the reference spectra using vegetation library (99). The library can workflow (THOR) of ‘Target Detection’ wizard is available. the combined score of SAM, BE, and be imported and exported as ASCII and . sli (native format). Metadata-rich spectral ENVI Also presented in THOR workflow, an SFF. ‘Library Builder’ can view, import, library (MRSL) can be created using interactive tool and export spectral plot data. THOR Spectral Library builder. Sequential performs matching by SAM, MF, ACE, spectral library architecture is considered NED, SSM, SID, CEM, OSP, TCIMF, as a limitation and UWD. ‘Material identification tool’ matches the unknown and reference spectrum using SAM. ‘Hyperspectral Material Identification Tool’ uses ACE and the background statistics of the target spectrum

Structure

Salient features of spectral matching tools.

Spectral matching tool

Table 5.



SPECMIN

The Spectral Geologist (TSG)

Anomaly detection, target detection, material mapping, and material identification are available under the Spectral Analyst tool Archive library window, spectrum plot, preprocessing, and mapping tools are present

Spectral Analysis Workstation in ERDAS Imagine


Remarks

(Continued )

Anomaly detection, target detection, Spectral libraries of USGS, JPL, and material mapping, and material ASTER are available. Libraries can also identification performed using an input be created in the native format (*.spl) of image and reference spectra and user’s ERDAS. SPECMIN spectral library, choice of matching measure (CEM, OSP, along with User Generated Libraries SAM, and SCM). Pixel values of the (UGL), can also be accessed. Authorized output (grey-scale image) correspond to users can utilize the library of Spectral the degree of match. Preprocessing, Information Technology Application viewing, creation, and addition of Center (SITAC) through ERDAS spectral libraries possible Comprises Summary, Log, Spectrum, ‘Spectrum Screen’ can be used to examine Used exclusively for geological analysis. Stack, Scatter, Tray, Hole Floater, and the single spectrum. ‘Log Screen’ Trial version and part of reference library Automated ID screens. The Spectral includes ancillary data. The Spectral available for downloading. Supports Assistant (TSA) and Aux Match modules Assistant (TSA) lists the best match spectral data from various are present in Automated ID screen based on the candidates in the inbuilt spectroradiometers. Project-specific spectral library. Mixtures of minerals are libraries can be created, imported, and also listed. Aux Match is a shape-based exported in TSG format matching tool that uses spectral mixtures as reference Comprises modules such as Tables-Search/ FSA provides the proportions of the target’s Includes USGS, JPL, and customized Match, Feature Search Analysis (FSA), spectral components. Table – Search/ libraries. Compatible with data from any and Indexes Match enables the display of spectral plot spectroradiometer. Spectral data can be and its related information. ‘Indexes’ exported into multiple data formats finds and views specific spectra along with ancillary data present in the library developed by Spectral International Inc. (2012)

Structure

(Continued ).


Table 5.



Structure

Comprises EZ- ID Real-time Mineral Identification tool, Library Builder module, and routines for vegetation indices

ore Xpress DARWin Spectral Data Acquisition software

(Continued ).


Table 5. Remarks

Scans the target spectrum, allows selection Exclusive software for analysing results of ‘Spectral Evolution’ radiometers. of regions of interest and displays the Portable version can be integrated with best match based on a weighted score. the radiometer during fieldwork. Inbuilt Reference spectra are from USGS, microphone, camera, and GPS allow the SPECMIN, and custom libraries. Batch collection of voice notes, photos, and mode processing of multiple spectra is possible and results can be saved onto location of each target spreadsheets and text files. ‘Library Builder’ adds new targets to the existing library. Metadata can be created or updated for each entry





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standardization and quality assurance methods in the cloud may result in difficulty in assessing the reliability of available data, thereby making it unsuitable for hyperspectral data users, recent examples like ‘mzCloud’ and the advent of data integrity measures render cloud computing a potential option for storing and analysing hyperspectral data. Easy access to spectral resources, automated software deployment, and unlimited storage capacity through this technology can provide a new dimension to the matching tools dealing with hyperspectral data sets.

7. Summary This review has been attempted after realizing the need for an appraisal of past developments, present progress, and future aspects in the domain of spectral matching. Various studies, theses, software programs, spectral tools, and libraries were reviewed. The limitations and advantages of existing spectral matching approaches were evaluated and discussed in this paper. It was realized that spectral matching tools and libraries have evolved in tandem with advancements in hyperspectral image acquisition and applications. Furthermore, the transition from manual and semi-automatic matching tools, such as USGS’s Tetracorder and ENVI’s Spectral Analyst, to automated target identification tools, such as ENVI’s Material Identification Workflow and ERDAS’s Spectral Analysis Workstation, have resulted in improved accuracy of target detection and material mapping. Although spectral matching tools individually possess unique capabilities, they have failed to resolve the issues related to spectral library, absorption features, illumination effects, false hits, distortion of spectra, and mixed pixels. Thus, it is inferred that a combination of spectral abilities of two or more algorithms such as SAM-SID mixed measure (Du et al. 2004), SID-SCA, (Naresh Kumar et al. 2011), and NS3 (Nidamanuri and Zbell 2011a) yield better matching performance than individual measures. This review has highlighted that most spectral matching libraries and applications have revolved around mineral mapping, agriculture and forestry, coastal and ocean bathymetry, ocean colour mapping, and planetary material mapping. Thus, the lack of any role in the fields of military applications, urban feature extraction, water quality mapping, and artefact removal is to be addressed. The operating principle and structure of commonly used spectral matching tools is also reviewed and listed to provide a brief account. Since continuous spectra of target materials form the most essential input of spectral matching, the need for spectral libraries has also been highlighted in this review. Furthermore, various works on collecting spectra, generating and compiling a library, and their usage in matching have been reviewed. In addition, a comprehensive study has been carried out on the method of construction, source of data, querying capability, metadata aspects, and applications of extensively used libraries such as USGS, ASTER, and RELAB. The emerging trend of developing a centralized database for uploading and sharing data from several sources, viz. SPECCHIO, has also been reported. The need for sensorspecific libraries pertaining to existing hyperspectral missions such as EO-1 Hyperion, CASI, PROBA/CHRIS, and the latest missions for environmental monitoring such as HICO and HyspIRI has been highlighted. The recent concept of ‘cloud’ for compiling large volumes of hyperspectral libraries and algorithms for efficient target identification has also been cited in this paper. Hence addressing and resolving these issues will increase the performance of spectral matching in hyperspectral image analysis.

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