Foreword to the Special Issue on Hyperspectral Remote ... - IEEE Xplore

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

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Foreword to the Special Issue on Hyperspectral Remote Sensing: Theory, Methods, and Applications I.

INTRODUCTION

A. Dimensionality Reduction and Feature Selection

Hyperspectral imaging has become a very active field in both theoretical and application-related research in remote sensing since its emergence in the 1980s [1], [2]. The first airborne hyperspectral sensor, called Airborne Imaging Spectrometer (AIS), was designed in the early 1980s [3]; an advanced airborne sensor, i.e., Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), began to provide high-quality airborne data in 1987 [4], [5]; Hyperion–the first spaceborne hyperspectral sensor–was launched in 2000 and routinely acquires data with larger spatial coverage from earth orbit [6]. Nowadays, more and more high-quality hyperspectral data are commercially available, which are greatly promoting the research in hyperspectral remote sensing. The fact that hyperspectral remote sensing is of great interest to the scientific community can be partially reflected by several journal special issues published in the last decade, such as those appeared in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) [7], IEEE Signal Processing Magazine (SPM) [8], and Remote Sensing of Environment (RSE) [9]. It can also be demonstrated by the lately launched but well-received workshop series, Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), sponsored by IEEE Geoscience and Remote Sensing Society. Since its first inaugural 2009 workshop in Grenoble, France, followed by the second one in Reykjavik, Iceland, in 2010, and the third one in Lisbon, Portugal in 2011, WHISPERS has been considered as one of the most prestigious international conferences in hyperspectral remote sensing. Some interesting research presented in WHISPERS series can be found in two recent journal special issues [10], [11]. In 2012, WHISPERS moved to Shanghai, China. There were 91 oral presentations and 31 posters. Three excellent plenary speakers: Professor Qingxi Tong from Peking University, China, Professor Jean-Pierre Bibring from University of Paris, France, and Professor Susan Ustin from University of California, Davis, USA, introduced their cutting-edge research in this field. Following the success of this fourth WHISPERS edition, it is our great pleasure to introduce this special issue in order to present the most recent developments. A large number of submissions were received, and 29 papers have been accepted after rigorous review. A few of the submissions will be published in the following issues of JSTARS, after the final reviews and revisions are completed. In the remainder of this Foreword, we briefly introduce these 29 accepted papers, which cover several important topics. In particular, 9 of these 29 papers are related to hyperspectral remote sensing applications, which well fit the scope of JSTARS. Digital Object Identifier 10.1109/JSTARS.2013.2257422

Due to large data volume and high spectral correlation, dimensionality reduction is often conducted before hyperspectral image analysis. Many researchers have found out that dimensionality reduction can not only reduce the computational cost in image analysis, it can also improve the performance of image analysis due to potential enhancement of useful data information during this process. Dimensionality reduction can be achieved by a transform-based approach or a band selection method. The former applies a linear or nonlinear transformation to combine the data in the original space, while the latter selects only a few of original bands for later use. In this special issue, a linear but data-independent dimensionality reduction method based on random projection is developed in [12]. In [13], a comparative analysis of six band selection methods is conducted for biophysical variable estimation problems. Note that some interesting papers on band selection can also be found in the JSTARS special issue following WHISPERS 2011 [14], [15]. It is an open problem on the optimal value of reduced dimensionality, or the number of bands that ought to be selected. This may be related to the intrinsic or virtual dimensionality of high-dimensional data [16], [17]. In this special issue, discussion on overestimation and technique on overcoming such a problem can be found in [18], and a new estimation method is proposed in [29]. B. Noise Estimation and Removal In many statistical signal processing techniques, random noise plays a critical role. Noise estimation in hyperspectral images remains as a challenging problem since noise may be both spatially and spectrally correlated. Hyperspectral image analysis can be improved by effective noise estimation and removal. For instance, it is demonstrated that denoising can improve the performance of band selection in [13]; in [18], it shows that a more accurate noise estimate can greatly facilitate intrinsic dimensionality estimation. In this special issue, two contributions are directly related to this topic. In [19], the widely used multiple linear regression (MLR)-based noise estimation methods are reviewed and compared, and it concludes that MLR in spectral domain is more important than spatial domain decorrelation. In [20], a sparse representation based noise reduction method is proposed, with the assumption that the signal component in an observed pixel can be approximated by only a small number of atoms in a dictionary while the noise component cannot; the proposed approach is suitable to both signal-independent and signal-dependent noises.

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C. Spectral Mixture Analysis Spectral mixture analysis (SMA) is very useful for hyperspectral image analysis. The related topics, such as endmember extraction and abundance estimation, have been widely studied. Some recent advances are presented in a special issue in TGRS [21], a review paper in [22], and other ones in the recent JSTARS special issue [23], [24]. The state-of-the-art methodologies include: extracting endmembers that are not image pixels, estimating abundances that can automatically satisfy the non-negativity and sum-to-one constraints, relaxing the data dimensionality limitation on abundance estimation via sparse regression, and nonlinear unmixing. This special issue includes six papers related to SMA. Endmember extraction are discussed in [25] with maximum simplex volume criterion and in [26] using ant colony optimization. Abundance estimation with a kernel weighted least squares method is developed in [27]. Simultaneously estimating endmembers and their abundances via non-negative matrix factorization is improved in [28]. In [29], a new method on intrinsic dimensionality estimation with nearest-neighbor distance ratios is proposed, whose result may be used as guidance in the determination of the number of endmembers. As mentioned earlier, it may also be used as reference for the reduced data dimensionality. Unmixed results, i.e., endmember abundance maps, can be used in many practical applications, such as soft classification, estimation of land surface parameters, etc. In [30], an interesting sub-pixel mapping technique utilizing endmember abundance maps is proposed to achieve super-resolution of the final output. D. Classification In hyperspectral image classification, the objective is often to assign a class label to each pixel. So it is mainly about “hard” or “crisp” pixel-based classification. With the advance of sensor technology, the spatial resolution of hyperspectral imagery becomes much finer, which makes the results from such label assignment more meaningful [31]. Some recent work can be found in a review paper published in RSE [32], and some comparative studies can be found in [33] and [34]. Thanks to the rapid advance in machine learning, the improvement of hyperspectral image classification is continued with the newly developed algorithms in semi-supervised learning, active learning, and transfer learning, etc. This special issue has three papers on classification. In [35], spectral derivative features are investigated for classification, and experimental results demonstrate that using the first order derivatives can significantly improve classification accuracies. In [36], an ensemble-based classification system is developed, which applies the mean-shift (MS) algorithm to randomly selected bands, classifies the MS maps with support vector machine, and then produces the fusion output using the ordered weighted averaging operator. In [37], classification is accomplished by procedures based on discriminant analysis and band selection for the simulated new generation of spaceborne hyperspectral data, i.e., PRISMA (PRecursore IperSpettrale della Missione Applicativa); it shows that using only 10 selected

bands excellent classification can be achieved for agriculture land use study. E. Target and Anomaly Detection In general, target detection is to detect small manmade targets from background clutters. When the target signature is unknown, it actually is to detect anomalies whose signatures are different from background. Target detection cannot be simply viewed as a two-class classification problem since the number of target pixels is very small that makes its statistical estimation very difficult. Background modeling for effective background suppression is the key for success. Some recent work can be found in [38]–[40]. Two papers in this special issue are related to supervised target detection: an existing target-constrained interference-minimized filter is kernelized for further improvement in [41], and a cointegration theory-based detector is proposed in [42]. Another two papers are related to anomaly detection: the use of multiple windows is proposed to replace the traditional dual-window technique in the original RX algorithm in [43], and nonlinear principle component analysis is applied for anomaly detection in [44]. F. High-Performance Computing High-performance computing technology can greatly expedite the processing of hyperspectral images [45], [46]. Recently, graphics computing units (GPUs) are of great interest to high-performance computing community because it can provide very high levels of computing performance at very low cost; in particular, it is suitable to real-time onboard processing due to its portability [47]. In this special issue, [48] describes GPU implementation of a predictive lossy algorithm originally designed for European Spatial Agency (ESA)-Exomar mission for onboard compression; it is inherently a data- and task-parallel algorithm, and hence amenable to a very efficient GPU implementation; using Nvidia’s CUDA parallel architecture, experimental results show significant speed-ups with respect to a single-threaded CPU implementation. Note that hyperspectral image compression is another widely studied topic [49], [50]. In addition, NVidia GPU with CUDA environment is also utilized in [26] in this special issue to expedite the endmember extraction process based on ant colony optimization. G. Applications The nine application papers range from hyperspectral remote sensing of agriculture, vegetation, forestry, and water resources with spaceborne and ground-based data. Paper [51] validates the existing corn critical nitrogen curve for the northwestern plain of Shandong Province, and studies the retrieval of nitrogen nutrition index from data collected by an Analytical Spectral Devices (ASD) spectroradiometer using principal component analysis and backpropagation neural network. It is known that hyperspectral remote sensing can be useful for crop stress detection. In [52], detection of wheat aphid, one of the most destructive pests that emerges in northwest China almost every year and impacts on the production of winter wheat,

FOREWORD TO THE SPECIAL ISSUE ON HYPERSPECTRAL REMOTE SENSING: THEORY, METHODS, AND APPLICATIONS

is studied; aphid density can be well estimated from ASD data via partial least square regression. In [53], a new estimation method for Lambert coefficients is proposed by taking advantage of shading distribution in leaf scale hyperspectral images of vegetation. It is shown that shading distribution in hyperspectral space is composed of specular, diffuse, and shadowed clusters. Lambert coefficient can be well derived from the first eigenvector of diffuse cluster. Experimental results show that chlorophyll indices based on the estimated Lambert coefficients are consistent with the growth stages of paddy fields. In [54], photochemical reflectance index (PRI), belonging to the family of normalized difference indices (NDIs), is calculated for coniferous canopies using ASD imaging spectroscopy data; experimental results show that PRI can be scaled reasonably well from needle to shoot level with a strictly positive scaling factor. In [55], a canopyleaf PROFLAIR model is inverted using Hyperion data to retrieve canopy effective leaf area index (LAI), leaf chlorophyll content (Ca+b), and canopy integrated chlorophyll content (LAI x Ca+b). The estimated variables are then compared with ground measurements collected in the field. The results show the ability of the PROFLAIR model to realistically simulate canopy spectral reflectance. In [56], polarization measurements are utilized for the study of a specific vegetation, i.e., Sedum spectabile Boreau, with a newly self-developed polarized field imaging spectrometer system (FISS-P). With the acquired polarized hyperspectral images, 10 polarization parameters of the vegetation are analyzed. Some of these parameters are very consistent for both spectral and spatial aspects, which means polarization-reflectance imagery may have the potential of replacing the traditional intensity-reflectance imagery. In [57], surface total suspended matter (TSM), total inorganic particles (TIP), and turbidity in the Pearl River in South China are estimated from Hyperion images using an exponential regression model and the first order spectral derivative. These estimates show high correlation with in-situ measurements. In [58], the influence of chlorophyll concentration on the bathymetric estimation error is investigated by using the MEdium Resolution Imaging Spectrometer (MERIS) data collected over the optically complex lagoon of New Caledonia. The bathymetric estimation method is based on the rotation of a pair of spectral bands. It is advised to use this method only in oligotrophic periods and regions where the chlorophyll concentration is low and constant to limit the estimation error. In [59], the water column effect on the detection and discrimination of coral species and habitats is investigated using simulated data. Both parametric (t-test) and non-parametric test (Wilcoxon rank sum test) are used to determine if significant difference exists between two spectral signatures. It is found that the separability is quickly diminished as depth increases to a certain level, although the pure spectra of coral species and habitats have distinct spectral features. By providing a snapshot of status, potentials, and challenges of hyperspectral remote sensing, we believe this special issue will stimulate new ideas and further efforts in this area. Finally,

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we would like to take this opportunity to sincerely thank all the contributors and peer reviewers whose valuable contributions made this special issue possible. QIAN DU, Guest Editor Department of Electrical and Computer Engineering Mississippi State University Mississippi State, MS USA LIANGPEI ZHANG, Guest Editor LIESMARS Lab Wuhan University Wuhan, China BING ZHANG, Guest Editor Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing, China XIAOHUA TONG, Guest Editor College of Surveying and GeoInformatics Tongji University Shanghai, China PEIJUN DU, Guest Editor Department of Geographical Information Science Nanjing University Nanjing, China JOCELYN CHANUSSOT, Editor-in-Chief GIPSA-Lab Grenoble Institute of Technology Grenoble, France REFERENCES [1] A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Sci., vol. 228, pp. 1147–1153, 1985. [2] A. F. H. Goetz, “Three decades of hyperspectral remote sensing of the Earth: A personal view,” Remote Sens. Environ., vol. 113, pp. S2–S16, 2009. [3] G. Vane, A. F.H. Goetz, and J. Wellman, “Airborne imaging spectrometer: A new tool for remote sensing,” IEEE Trans. Geosci. Remote Sens., vol. GE-22, pp. 546–549, 1984. [4] G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ., vol. 44, no. 2-3, pp. 127–143, 1993. [5] R. O. Green, M. L. Eastwood, C. M. Sarture, T. G. Chrien, M. Aronsson, B. J. Chippendale, J. A. Faust, B. E. Pavri, C. J. Chovit, M. Solis, and M. R. Olah, “Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ., vol. 65, no. 3, pp. 227–248, 1998. [6] J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 6, pp. 1160–1173, Jun. 2003. [7] D. A. Landgrebe, S. B. Serpico, M. M. Crawford, and V. Singhroy, “Introduction to the special issue on analysis of hyperspectral image data,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1343–1345, Jul. 2001. [8] G. Shaw and D. Manalakis, “Signal processing for hyperspectral image exploitation,” IEEE Signal Processing Mag., vol. 19, no. 1, pp. 12–16, Jan. 2002.

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Qian (Jenny) Du (S’98–M’00–SM’05) received the Ph.D. degree in electrical engineering from the University of Maryland Baltimore County, Baltimore, MD, USA, in 2000. She was with the Department of Electrical Engineering and Computer Science, Texas A&M University, Kingsville, TX, USA, from 2000 to 2004. She joined the Department of Electrical and Computer Engineering at Mississippi State University, Starkville, MS, USA, in Fall 2004, where she is currently an Associate Professor. Her research interests include hyperspectral remote sensing image analysis, pattern classification, data compression, and neural networks. Dr. Du currently serves as Co-Chair for the Data Fusion Technical Committee of IEEE Geoscience and Remote Sensing Society (GRSS), and Chair for Remote Sensing and Mapping Technical Committee of International Association for Pattern Recognition. She also serves as Associate Editor for IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING and Associate Editor for IEEE Signal Processing Letters. She received the 2010 Best Reviewer award from IEEE GRSS.

Liangpei Zhang (M’06–SM’08) received the B.S. degree in physics from Hunan Normal University, Changsha, China, in 1982, the M.S. degree in optics from the Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China, in 1988, and the Ph.D. degree in photogrammetry and remote sensing from Wuhan University, Wuhan, China, in 1998. He is currently the Head of the Remote Sensing Division, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing at Wuhan University. He is also a ChangJiang Scholar Chair Professor appointed by the Ministry of Education of China. He is currently a Principal Scientist for the China State Key Basic Research Project (2011–2016) appointed by the Ministry of National Science and Technology of China to lead the remote sensing program in China. He has published more than 260 research papers. He holds five patents. His research interests include hyperspectral remote sensing, high-resolution remote sensing, image processing, and artificial intelligence. Dr. Zhang is a Fellow of the IEE, Executive Member (Board of Governors) of the China National Committee of International Geosphere–Biosphere Programme, and Executive Member of the China Society of Image and Graphics. He regularly serves as a Co-chair of the series SPIE Conferences on Multispectral Image Processing and Pattern Recognition, Conference on Asia Remote Sensing, and many other conferences. He edits several conference proceedings, issues, and geoinformatics symposiums. He also serves as an Associate Editor of IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, International Journal of Ambient Computing and Intelligence, International Journal of Image and Graphics, International Journal of Digital Multimedia Broadcasting, Journal of Geo-spatial Information Science, and Journal of Remote Sensing. He is the General Chair of the 4th GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

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Bing Zhang (SM’12) received the B.S. degree in geography from Peking University, Shenzhen, China, and the M.S. and Ph.D. degrees in remote sensing from the Institute of Remote Sensing Applications, Chinese Academy of Sciences (CAS), Beijing, China. Currently, he is Professor and Deputy Director of the Institute of Remote Sensing and Digital Earth at the Chinese Academy of Sciences. He specializes in hyperspectral remote sensing and has more than 18 years of experience in studying and graduate education in this field. His research interests include development of physics-based models and image processing software for the use of hyperspectral remote sensing data in solving problems in geology, hydrology, ecology and botany. He has authored or co-authored around 150 publications, including more than 100 journal citation papers and more than 50 peer-reviewed international conference papers. He has written four books in Chinese in hyperspectral remote sensing area, including Hyperspectral Remote Sensing, Hyperspectral Image Classification and Target Detection, Hyperspectral Remote Sensing for Inland Water, and Hyperspectral Remote Sensing and its Multidisciplinary Applications. Due to his innovative research and research-based development projects, he has received six Chinese National, Ministerial and Provincial S&T progress awards. Dr. Zhang currently serves as an Associate Editor for IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING.

Xiaohua Tong received the Ph.D. degree from Tongji University, Shanghai, China, in 1999. Currently, he is a Professor and Dean in the College of Surveying and Geo-informatics at Tongji University. He worked as a Postdoctoral Researcher in the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China, between 2001 and 2003. He was a Research Fellow in The Hong Kong Polytechnic University, Kowloon, Hong Kong, in 2006. He was a Visiting Scholar at the University of California, Santa Barbara, CA, USA, during 2008–2009. His research interests include remote sensing, geographic information systems, uncertainty and spatial data quality, image processing for high-resolution and hyperspectral images. Dr. Tong currently serves as the Vice Chair of the Commission on Spatial Data Quality of International Cartographical Association, and Co-Chair of ISPRS WG II/4 “Spatial statistics and uncertainty modeling”. He is a Council Member of the Chinese Association of Geographical Information Systems.

Peijun Du (M’07–SM’12) is a Professor of photogrammetry, remote sensing, and geographical information science at the Department of Geographical Information Science, Nanjing University, Nanjing, China. After receiving the Ph.D. degree from China University of Mining and Technology in 2001, he was a teacher with the same university until he joined Nanjing University in 2011. He was a postdoctoral fellow at Shanghai Jiao Tong University from February 2002 to March 2004, and was a visiting scholar at the University of Nottingham, Nottingham, U.K., from November 2006 to November 2007. His research interests are remote sensing image processing and pattern recognition; remote sensing applications; hyperspectral remote sensing information processing; multi-source geospatial information fusion and spatial data handling; integration and applications of geospatial information technologies; and environmental information science (environmental informatics). He has published nine textbooks in Chinese and more than 100 research articles about remote sensing and geospatial information processing and applications. Dr. Du is the Co-chair of the Technical Committee, URBAN 2009 (the 5th IEEE GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas), and the Co-chair of the local organizing committee of JURSE 2009. He is also a member of International Society of Environment Information Science and IEEE GRSS, and a council member of China Society for Image and Graphics (CSIG), China Association for Remote Sensing Applications (CARSA) and Jiangsu Provincial Society of Remote Sensing and Geographic Information System. He also serves as the member of scientific committee or local organizing committee of other international conferences, for example, Accuracy 2008, ACRS 2009, WHISPERS 2010, 2011 and 2012, URBAN 2011, MultiTemp 2011, and ISDIF 2011. He is currently an Associate Editor for IEEE Geoscience and Remote Sensing Letters.


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Jocelyn Chanussot (M’04–SM’04–F’12) received the M.Sc. degree in electrical engineering from the Grenoble Institute of Technology (Grenoble INP), Grenoble, France, in 1995, and the Ph.D. degree from Savoie University, Annecy, France, in 1998. In 1999, he was with the Geography Imagery Perception Laboratory for the Delegation Generale de l’Armement (DGA—French National Defense Department). Since 1999, he has been with Grenoble INP, where he was an Assistant Professor from 1999 to 2005, an Associate Professor from 2005 to 2007, and is currently a Professor of signal and image processing. He is conducting his research at the Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab). His research interests include image analysis, multicomponent image processing, nonlinear filtering, and data fusion in remote sensing. Dr. Chanussot is the founding President of IEEE Geoscience and Remote Sensing French chapter (2007–2010) which received the 2010 IEEE GRS-S Chapter Excellence Award. He was the co-recipient of the NORSIG 2006 Best Student Paper Award, the IEEE GRSS 2011 Symposium Best Paper Award, the IEEE GRSS 2012 Transactions Prize Paper Award and the IEEE GRSS 2013 Highest Impact Paper Award. He was a member of the IEEE Geoscience and Remote Sensing Society AdCom (2009–2010), in charge of membership development. He was the General Chair of the first IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing (WHISPERS). He was the Chair (2009–2011) and Cochair of the GRS Data Fusion Technical Committee (2005–2008). He was a member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society (2006–2008) and the Program Chair of the IEEE International Workshop on Machine Learning for Signal Processing (2009). He was an Associate Editor for the IEEE Geoscience and Remote Sensing Letters (2005–2007) and for Pattern Recognition (2006–2008). Since 2007, he has been an Associate Editor for the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. Since 2011, he has been the Editor-in-Chief of the IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING.