An Update on the MATLAB Hyperspectral Image ...

An Update on the MATLAB Hyperspectral Image Analysis Toolbox Samuel Rosario-Torres,Emmanuel Arzuaga-Cruz, Miguel Velez-Reyes and Luis O. Jiménez-Rodríguez Laboratory for Applied Remote Sensing and Image Processing. Center for Subsurface Sensing and Imaging Systems University of Puerto Rico at Mayagüez, P. O. Box 9042, Mayagüez, Puerto Rico 00681

Abstract The Hyperspectral Image Analysis Toolbox (HIAT) is a collection of algorithms that extend the capability of the MATLAB numerical computing environment for the processing of hyperspectral and multispectral imagery. The purpose of the HIAT Toolbox is to provide information extraction algorithms to users of hyperspectral and multispectral imagery in environmental and biomedical applications. HIAT has been developed as part of the NSF Center for Subsurface Sensing and Imaging (CenSSIS) Solutionware that seeks to develop a repository of reliable and reusable software tools that can be shared by researchers across research domains. HIAT provides easy access to supervised and unsupervised classification algorithms developed at LARSIP over the last 8 years. Keywords-Hyperspectral; Image Analysis; Supervised Classification; Unsupervised Classification.

1. INTRODUCTION Hyperspectral Image analysis is supported by a variety of available software packages. The best known commercial product is the Environment for Visualizing Images (ENVI) [1] of Research Systems Inc., a Kodak subsidiary. ENVI provides code extensibility through the Interactive Data Language (IDL), allowing the possibility for routine and features expandability. Among the educational non-commercial products, the best known is MultiSpec [2] developed at Purdue University by Dr. David Landgrebe and the Remote Sensing research group in Purdue’s LARS. Multispec provides similar features to ENVI but does not provide extensibility. At UPRM, researchers at the Laboratory for Applied Remote Sensing and Image Processing (LARSIP) have been working on multi and hyperspectral image processing for over 8 years. To support researchers in environmental and biomedical applications using multi/hyperspectral imagery at UPRM LARSIP and at the Center for Subsurface Sensing and Imaging Systems (CenSSIS), a toolbox that incorporated the algorithms developed at LARSIP along with standard algorithms for classification similar to those included in ENVI and MultiSpec in the MATLAB platform widely used in engineering and science was necessary. HIAT includes original work in the areas of feature extraction/selection [3], [4] and contextual information classification enhancement [5] developed at UPRM. This paper presents the second release of HIAT. The algorithms implemented in the toolbox were developed during the last years in research projects sponsored by NASA-TCESS, DoD, ARMY TEC, and NSF. Different teams of students developed these algorithms. The key objective of the original work was primarily proof of concept demonstrations. In order to reach a wider audience in environmental and biomedical applications and to be able to use the algorithms in the processing of larger data sets within the MATLAB environment, we worked on the integration, optimization, robustness and user interfaces of the original prototypes. HIAT is aimed to be the common application for researchers in CenSSIS and LARSIP that use hyperspectral and multispectral imagery in different applications. The following sections will describe the previous HIAT prototypes developed at LARSIP, the modified toolbox, and an example how to use HIAT. HIAT is available for download at http://www.censsis.neu.edu/software/hyperspectral/Hyperspectoolbox.html.

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, edited by Sylvia S. Shen, Paul E. Lewis, Proceedings of SPIE Vol. 5806 (SPIE, Bellingham, WA, 2005) · 0277-786X/05/$15 · doi: 10.1117/12.605674

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2. HYPERSPECTRAL IMAGE ANALYSIS TOOLBOX The Hyperspectral Toolbox is currently being developed as an element of the CenSSIS Solutionware framework [11]. The objective of the CenSSIS Solutionware team is to develop a set of catalogued tools and toolsets that will provide for the rapid construction of a range of subsurface algorithms and applications. Solutionware tools will span toolboxes, visualization toolsets, database systems and application-specific software systems that have been developed in the Center. To facilitate programming of this toolbox, languages such as MATLAB are most appropriate. By programming at a high level of abstraction, the programmer can quickly construct a set of algorithms to solve a problem. Also, MATLAB is capable of providing a framework for proper software engineering practices to be followed. In addition, MATLAB provides portability of the code in the different platforms in which MATLAB works Windows family, Mac OS and UNIX systems. HIAT is used within an optimized MATLAB environment. It provides useful image analysis techniques for educational and research purposes, allowing the interaction and development of new algorithms, data management, results comparisons and post-processing. It is easy-to-use and powerful software for the CenSSIS community involved with HIAT. This toolbox implemented in MATLAB serves as an excellent educational resource for student laboratories, hence improving the classroom experience with graphical examples. A similar approach has been taken in the development of Multi-View Tomography Toolbox, another CenSSIS Solutionware application [7].

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Figure 1. Data Processing Schema for HIAT ver 1.0

2.1. PREVIOUS VERSION FUNCTIONALITIES Figure 1 shows the processing schema of HIAT previous version (V 1.0). The processing phases of HIAT were divided into 3 groups Feature Selection/Extraction, Classification and Post Processing. As Figure 1 shows, data could be processed with feature selection/extraction algorithms (or not) before the classification and to enhance the classification map we could use post processing algorithms. Feature Selection/Extraction algorithms provide users the ability to reduce the dimensionality of the HSI data.. The available algorithms are: • Principal Components Analysis [8][9] • Discriminant Analysis [8][9] • Singular Value Decomposition Band Selection [3][4] • Information Divergence Band Subset Selection [4] • Information Divergence Projection Pursuit [10] • Optimized Information Divergence Projection Pursuit [4]

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In the toolbox, standards classifiers are included. In addition, supervised and unsupervised versions of each classifier are included. Supervised classification process allows the users to select the training and testing samples for the spectral classes represented in the image or the users can load the training and testing samples stored previously. In unsupervised classification, the users can select the threshold and stopping criteria to stop the algorithms. The classifiers are: • Euclidean Distance • Fisher’s Linear Discriminant • Mahalanobis Distance • Maximum Likelihood • Angle Detection • Fuzzy Maximum Likelihood • Fuzzy Euclidean Distance Post Processing techniques integrate contextual information of the scene into the resulting classification map, this integration results in an increase on the classification accuracy [5]. Integrated to the toolbox are: • Supervised & Unsupervised Extraction and Classification of Homogeneos Objects (ECHO)[5] ƒ Window size 2x2, 3x3 and 4x4 The GUI of the older version of the toolbox is shown in Figure 2. As we can see, the GUI was composed of a main window in which the image being analyzed is displayed. Besides the main window, there are also sub module windows that include the supervised classification and unsupervised classification modules.

Figure 2. HIAT: GUI Visualization.

One of the advantages of the toolbox has to offer is that the user can apply supervised or unsupervised classification algorithms and routines to the same image in the same application in order of doing a more extensive analysis of the data they wish to analyze. MATLAB version 6.5 was used for the implementation of the HIAT. We are currently conducting tests using MATLAB version 7.0 to ensure the toolbox is fully upward compatible.

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Multi-Hyperspectral Data

Image Enhancement

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Classification

Classification Map

Post Processing

Figure 3. Data Processing Schema for HIAT.

2.2. ADDED FUNCTIONALITIES Figure 3 show data processing schema for the new version of HIAT. The image enhancement process included in HIAT is based on improving signal to noise rates by filtering noise using: • Principal Components Analysis Filter [8][9] • Resolution Enhancement [12]

Figure 4. HIAT GUI.

In HIAT main Graphical User Interface (GUI), see Figure 4, a box including some information of the opened image was added, also the menu was modified to be more similar to the toolbox processing scheme. The toolbox has been tested to guarantee minimum bugs in the release. 3. USING THE TOOLBOX: AN EXAMPLE A graphical user interface (GUI) in MATLAB has been developed in order to facilitate the use of the HIAT routines. The main window displays the image once it is loaded. All the interaction between HIAT and the user is done through the GUI. Advanced users have the alternative of calling the functions from the MATLAB command window.

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As an example, we will show how to use the toolbox to analyze an AVIRIS image. This image was acquired from AVIRIS, a high resolution hyperspectral imager with 220 spectral bands (.4 to 2.5 µm) and a 2-20m resolution (depending of the altitude). The area of interest and its classification map are shown in Figure 5 (from [13]). This image covers some area of using a portion of an AVIRIS data set taken over NW Indiana's Indian Pine test site in June 1992 [13]. With this image, we will give an example of how to use the HIAT to identify different targets characteristics. The first step is to load an image file. The supported image file formats are: • • • •

MATLAB (.mat) Remote Sensing binary (bil, bis, bsq) JPG TIFF

Figure 5. AVIRIS Image of Indiana’s Indian Pine and Ground Truth Map taken form [13].

Once loaded, the image can be viewed. The visualization of the image can be done band by band in a grayscale color map. There is a scroll bar for the user to browse the bands. If the user wants to visualize an RGB composite of the image, users can set the desired RGB bands for visualization. Figure 6 shows the default (band by band) visualization features (a) and the RGB view (b). Figure 7 shows the image enhancement process. Figure 7 (a) shows the original image with the parameter estimation of the cutoff frequency to enhance spectrally the image as shown in Figure 7 (b). Once we have enhance the image, if the image have high dimensionality we can use one of the features extraction/selection algorithms to reduce the dimensionality of the data. Figure 8 (a) (b) shows the process dimension reduction using the SVDSS algorithm. Figure 9 shows the supervised classification GUI, the areas selected as testing and training data for the different classes are shown. In this example four classes where selected, corn-notil, grass, soybean and background as shown in Figure 9. Once testing and training areas are selected for each class, users can save the areas in a file for future reference. Finally, the users select the desired classifier to analyze the results obtained from the input data. Figure 10 (a) shows the results obtained using the Maximum Likelihood distance classifier. HIAT provides post processing techniques to enhance the results of the obtained classification map you can finish there or continue with post-processing. Users have the option of selecting the different window size for ECHO. Figure 10 (b) shows the results obtained using ECHO with a window size of 3x3.

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(b) Figure 6. (a) grayscale view. (b) RGB view.

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Figure 7. (a) Original Image. (b) Enhance with Resolution Enhancemet.

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Figure 8. (a) Enhaced Image. (b) Subset Enhace Image with SVDSS.

Figure 9. Supervised Classification GUI

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Image classified using Supervised Maximum Likelihood

Image classified using 3X3 ECHO Classifier

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(a) Classmap obtained with Euclidean Distance classifier. (b) Post Processed Image with ECHO 3x3.

3.1. OTHER FUNCIONALITIES The toolbox includes others functionalities useful in multi/hyperspectral image processing such as class statistics, 3D visualization and pixel spectral response. The statistic GUI gives the users the option of view the means and the correlation matrix for each of the classes previously selected. Figure 11 (a) shows and example of the statistics GUI. Figure 11 (b) shows the visualization cube of the loaded data, as it shows users can rotate the cube to view a different angle of the data cube. In addition, users have the option of viewing the spectral response of a single pixel showing also the spectral response of the neighborhood and the average. Figure 12 shows an example of the pixel response tool. Finally if the users need some kind of guide or example, the toolbox has an online help to introduce the users to the use and functionalities of HIAT. Figure 13 shows the online help.

(a) Figure 11.

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(b) (a) Statistic GUI. (b) 3D Data Visualization.

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Figure 12.

Figure 13.

Pixel Spectral Response

HSI Toolbox help window.

4. FINAL REMARKS A MATLAB Toolbox for hyperspectral image analysis was presented. This toolbox is intended for researchers doing multi/hyperspectral image analysis in various fields. The HIAT provides a unified framework that gives support to these different disciplines. This framework facilitates the dissemination of research and development working new multi/hyperspectral image analysis algorithms.

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5. ACKNOWLEDGEMENTS The toolbox development work and some of the algorithms development work was partially supported by the NSF Engineering Research Centers Program under grant EEC-9986821. Some of the algorithm development work was supported by the NASA University Research Centers Program under grant NCC5-518, the Department of Defense under DEPSCoR Grant DAAG55-98-1-0016 and NIMA grant NMA2110112014. REFERENCES [1] [2] [3] [4]

[5] [6] [7] [8] [9] [10] [11] [12] [13]

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Research Systems Inc., ENVI, The environment for visualizing images, url: http://www.rsinc.com/envi/. Landgrebe, D., Biehl, L., MultiSpec, image spectral analysis url: http://www.ece.purdue.edu/~biehl/MultiSpec/description.html. Vélez M. and Jiménez L. “Subset Selection Analysis for the Reduction of Hyperspectral Imagery”, Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. IEEE International Volume 3, pp 1577 -1581, 1998. Arzuaga-Cruz, E., Jimenez-Rodriguez, L. O., and Velez-Reyes, M. “Unsupervised Feature Extraction and Band Subset Selection techniques based on Relative Entropy Criteria for Hyperspectral data Analysis”, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX. SPIE Proceedings Volume 5093, pp 462-473, 2003. Rivera-Medina, J. L., “Development of an Unsupervised Extraction and Classification of Homogeneous Objects for Hyperspectral Images”, CpE Masters’ Thesis, University of Puerto Rico at Mayagüez, PR, 2003. I. Sommerville, “Software Engineering”, 5th edition, Addison-Wesley, 1998. Kaeli, D., et al., The Multi-view Tomography Toolbox, url: www.censsis.neu.edu/mvt/MVT.html., Center for Subsurface Sensing and Imaging Systems, Northeastern University, Boston, MA, 2003. Fukunaga, K., Introduction to Statistical Pattern Recognition, 2nd edition, San Diego, CA, 1990. Richards, J.A., Remote Sensing Digital Image Analysis, An Introduction, 3rd Edition, New York: Springer 1999. Ifarraguerri, A. and CHIATng, C., “Unsupervised Hyperspectral Image Analysis with Projection Pursuit”, IEEE Transactions on Geoscience and Remote Sensing, vol 38, no. 6, pp 2529-2538, 2000. Aruaga-Cruz, E. Jiménez-Rodríguez, L., Vélez-Reyes, M. and et. al. “A MATLAB Toolbox for Hyperspectral Image Analysis”. In Proceedings of International Geoscience and Remote Sensing Symposium, September 2004. Hunt, S. D., Laracuente, J. “Determining noise in hyperspectral imagery for the application of oversampling to supervised classification”. Tadjudin S. and Landgrebe, D. A. “Robust Parameter Estimation for Mixture Model”. IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 1, pp. 439-445, January 2000.

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