Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects

Hongjun SU

doi:10.11834/jrs.20210354

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Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects
Vol. 26, Issue 8, Pages: 1504-1529(2022)
Published： 07 August 2022 ，
DOI： 10.11834/jrs.20210354

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苏红军.2022.高光谱遥感影像降维：进展、挑战与展望.遥感学报，26（8）： 1504-1529

Su H J. 2022. Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects. National Remote Sensing Bulletin， 26（8）：1504-1529
苏红军.2022.高光谱遥感影像降维：进展、挑战与展望.遥感学报，26（8）： 1504-1529 DOI： 10.11834/jrs.20210354.

Su H J. 2022. Dimensionality reduction for hyperspectral remote sensing： Advances， challenges， and prospects. National Remote Sensing Bulletin， 26（8）：1504-1529 DOI： 10.11834/jrs.20210354.

摘要

高光谱遥感影像数据具有高维特征、信息冗余、不确定性显著、小样本、空谱合一等特征，对其进行数据处理面临巨大挑战，高光谱遥感影像降维是高光谱遥感的重要研究方向之一。本文对当前高光谱遥感影像降维的相关研究进展进行了综述，在介绍高光谱遥感数据特点的基础上，重点从特征提取和特征选择两方面对高光谱遥感影像降维的最新研究和前沿进展进行了系统性综述；并从特征可分性、特征质量评价、特征数目确定、多特征优化以及需求驱动的特征选择等方面分析了高光谱遥感影像降维面临的挑战。随着智能化高光谱遥感的发展，高光谱遥感影像智能降维成为未来的发展方向，同时其发展将兼顾多特征质量评估与优选、搜索策略优化、满足应用需求等多目标的需求。随着高光谱遥感数据获取能力的提升和深入应用，高光谱遥感影像降维将会发挥重要而不可替代的作用。

Abstract

Hyperspectral imaging can provide narrow bands and continuous spectrum information. However

hyperspectral image data have the characteristics of high dimensionality

rich features

information redundancy

small samples

and significant uncertainty

which result in difficulties in hyperspectral image data processing. Dimensionality reduction of hyperspectral remote sensing is one of the important topics in hyperspectral image data processing. Hyperspectral image data have hundreds of bands and can provide rich information

but a strong correlation exists between different bands

resulting in data redundancy. Therefore

the dimensionality problem is encountered during the processing of hyperspectral data

such as the increase in time complexity and the overfitting of the prediction model due to the increase in spectral feature dimension. More importantly

the number of training samples available for hyperspectral remote sensing images is small

and the feature dimension is much larger than the training sample. The classification accuracy will increase first and then decrease with the increase of feature dimensionality

that is

the “Hughes” phenomenon. Therefore

exploiting the rich information of hyperspectral images data and solving the problem of high feature dimension through certain methods have become key issues in the research on hyperspectral imaging data processing. The dimensionality reduction of hyperspectral remote sensing image is an approach to reduce the dimensionality of hyperspectral imaging through feature extraction or band selection while retaining as much effective information or features as possible. Feature extraction methods

such as principal component analysis

linear discriminant analysis

independent component analysis

manifold learning

and deep learning-based methods

use the projection transformation method to map hyperspectral data from high-dimensional space to low-dimensional space. Feature selection eliminates redundant bands without changing the original feature structure and finds representative feature band subsets

such as the selection based on information measurement and feature correlation. With the development of new technologies

evolutionary and intelligent algorithms

such as the genetic

ant colony

and firefly algorithms

have been applied in hyperspectral remote sensing dimensionality reduction.

This article systematically summarizes and reviews the current advances in dimensionality reduction for hyperspectral remote sensing

especially for feature extraction and selection. For feature extraction

we review the advances of feature extraction algorithms based on index and parameters

projection and transformation

band combination

spatial algorithm

manifold learning

and deep learning. For band selection

the advances in information measurement

search strategy

optimized band number

multi-feature quality assessment

and optimization algorithms are reviewed. The challenges of dimensionality reduction for hyperspectral remote sensing are analyzed from five aspects: feature separability

feature quality evaluation

feature number determination

multi-feature optimization

and problem-oriented feature selection. Intelligent dimensionality reduction will be one of the most popular topics with the development of intelligent hyperspectral remote sensing. Meanwhile

multi-feature quality assessment

search strategy optimization and application requirements will attract special attention in the future. The dimensionality reduction of hyperspectral remote sensing will play an important and irreplaceable role in hyperspectral image data acquisition and applications.

关键词

高光谱遥感数据降维特征提取特征选择多特征优化

Keywords

hyperspectral remote sensingdimensionality reductionfeature extractionfeature selectionmultiple features optimization

references

Alsuwaidi A, Grieve B and Yin H J. 2018. Feature-ensemble-based novelty detection for analyzing plant hyperspectral datasets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(4): 1041-1055 [DOI: 10.1109/JSTARS.2017.2788426http://dx.doi.org/10.1109/JSTARS.2017.2788426]

Bajorski P. 2011. Second moment linear dimensionality as an alternative to virtual dimensionality. IEEE Transactions on Geoscience and Remote Sensing, 49(2): 672-678 [DOI: 10.1109/TGRS.2010.2057434http://dx.doi.org/10.1109/TGRS.2010.2057434]

Baudat G and Anouar F. 2000. Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10): 2385-2404 [DOI: 10.1162/089976600300014980http://dx.doi.org/10.1162/089976600300014980]

Bellman R E. 1961. Adaptive Control Processes: A Guided Tour. Princeton, NJ: Princeton University Press

Benediktsson J A, Sveinsson J R and Amason K. 1995. Classification and feature extraction of AVIRIS data. IEEE Transactions on Geoscience and Remote Sensing, 33(5): 1194-1205 [DOI: 10.1109/36.469483http://dx.doi.org/10.1109/36.469483]

Bioucas-Dias J M, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N and Chanussot J. 2013. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 1(2): 6-36 [DOI: 10.1109/MGRS.2013.2244672http://dx.doi.org/10.1109/MGRS.2013.2244672]

Camastra F and Staiano A. 2016. Intrinsic dimension estimation: advances and open problems. Information Sciences, 328: 26-41 [DOI: 10.1016/j.ins.2015.08.029http://dx.doi.org/10.1016/j.ins.2015.08.029]

Challa A, Barman G, Danda S and Sagar B S D. 2022. Band selection using dilation distances. IEEE Geoscience and Remote Sensing Letters, 19: 5503705 [DOI: 10.1109/LGRS.2021.3057117http://dx.doi.org/10.1109/LGRS.2021.3057117]

Champion I, Germain C, Da Costa J P, Alborini A and Dubois-Fernandez P. 2014. Retrieval of forest stand age from SAR image texture for varying distance and orientation values of the gray level co-occurrence matrix. IEEE Geoscience and Remote Sensing Letters, 11(1): 5-9 [DOI: 10.1109/LGRS.2013.2244060http://dx.doi.org/10.1109/LGRS.2013.2244060]

Chang C I. 2000. An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis. IEEE Transactions on Information Theory, 46(5): 1927-1932 [DOI: 10.1109/18.857802http://dx.doi.org/10.1109/18.857802]

Chang C I. 2003. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Springer [DOI: 10.1007/978-1-4419-9170-6http://dx.doi.org/10.1007/978-1-4419-9170-6]

Chang C I. 2009. Virtual dimensionality for hyperspectral imagery. SPIE Newsroom, 52(1): 188-208 [DOI: 10.1117/2.1200909.1749http://dx.doi.org/10.1117/2.1200909.1749]

Chang C I. 2013. Hyperspectral Data Processing: Algorithm Design and Analysis. New Jersey: Wiley-Interscience

Chang C I. 2018. A review of virtual dimensionality for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(4): 1285-1305 [DOI: 10.1109/JSTARS.2017.2782706http://dx.doi.org/10.1109/JSTARS.2017.2782706]

Chang C I and Du Q. 1999. Interference and noise-adjusted principal components analysis. IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2387-2396 [DOI: 10.1109/36.789637http://dx.doi.org/10.1109/36.789637]

Chang C I and Du Q. 2004. Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 608-619 [DOI: 10.1109/TGRS.2003.819189http://dx.doi.org/10.1109/TGRS.2003.819189]

Chang C I, Kuo Y M, Chen S H, Liang C C, Ma K Y and Hu P F. 2021. Self-mutual information-based band selection for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 59(7): 5979-5997 [DOI: 10.1109/TGRS.2020.3024602http://dx.doi.org/10.1109/TGRS.2020.3024602]

Chang C I, Lee L C, Xue B, Song M P and Chen J. 2017. Channel capacity approach to hyperspectral band subset selection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10): 4630-4644 [DOI: 10.1109/JSTARS.2017.2724604http://dx.doi.org/10.1109/JSTARS.2017.2724604]

Chang C I and Liu K H. 2014. Progressive band selection of spectral unmixing for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(4): 2002-2017 [DOI: 10.1109/TGRS.2013.2257604http://dx.doi.org/10.1109/TGRS.2013.2257604]

Chang C I and Wang S. 2006. Constrained band selection for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1575-1585 [DOI: 10.1109/TGRS.2006.864389http://dx.doi.org/10.1109/TGRS.2006.864389]

Chen S G and Zhang D Q. 2011. Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 8(2): 369-373 [DOI: 10.1109/LGRS.2010.2076407http://dx.doi.org/10.1109/LGRS.2010.2076407]

Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094-2107 [DOI: 10.1109/JSTARS.2014.2329330http://dx.doi.org/10.1109/JSTARS.2014.2329330]

Coban M Z and Mersereau R M. 1998. A fast exhaustive search algorithm for rate-constrained motion estimation. IEEE Transactions on Image Processing, 7(5): 769-773 [DOI: 10.1109/83.668031http://dx.doi.org/10.1109/83.668031]

Conoscenti M, Coppola R and Magli E. 2016. Constant SNR, rate control, and entropy coding for predictive lossy hyperspectral image compression. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7431-7441 [DOI: 10.1109/TGRS.2016.2603998http://dx.doi.org/10.1109/TGRS.2016.2603998]

Dadon A, Ben-Dor E and Karnieli A. 2010. Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (smile) in Hyperion images. IEEE Transactions on Geoscience and Remote Sensing, 48(6): 2603-2612 [DOI: 10.1109/TGRS.2010.2040391http://dx.doi.org/10.1109/TGRS.2010.2040391]

Debba P, Carranza E J M, Van der Meer F D and Stein A. 2006. Abundance Estimation of spectrally similar minerals by using derivative spectra in simulated annealing. IEEE Transactions on Geoscience and Remote Sensing, 44(12): 3649-3658 [DOI: 10.1109/TGRS.2006.881125http://dx.doi.org/10.1109/TGRS.2006.881125]

Dong Y N, Du B, Zhang L P and Zhang L F. 2017. Dimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning. IEEE Transactions on Geoscience and Remote Sensing, 55(5): 2509-2524 [DOI: 10.1109/TGRS.2016.2645703http://dx.doi.org/10.1109/TGRS.2016.2645703]

Dopido I, Villa A, Plaza A and Gamba P. 2012. A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2): 421-435 [DOI: 10.1109/JSTARS.2011.2176721http://dx.doi.org/10.1109/JSTARS.2011.2176721]

Du P J, Tan K and Xia J S. 2012. Hyperspectral Image Classification and SVM Applications. Beijing: Science Press

杜培军, 谭琨, 夏俊士. 2012. 高光谱遥感影像分类与支持向量机应用研究. 北京: 科学出版社

Du P J, Wang X M, Tan K and Xia J S. 2011. Dimensionality reduction and feature extraction from hyperspectral remote sensing imagery based on manifold learning. Geomatics and Information Science of Wuhan University, 36(2): 148-152

杜培军, 王小美, 谭琨, 夏俊士. 2011. 利用流形学习进行高光谱遥感影像的降维与特征提取. 武汉大学学报(信息科学版), 36(2): 148-152 [DOI: 10.13203/j.whugis2011.02.027http://dx.doi.org/10.13203/j.whugis2011.02.027]

Du Q. 2007. Modified fisher's linear discriminant analysis for hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 4(4): 503-507 [DOI: 10.1109/LGRS.2007.900751http://dx.doi.org/10.1109/LGRS.2007.900751]

Du Q and Yang H. 2008. Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geoscience and Remote Sensing Letters, 5(4): 564-568 [DOI: 10.1109/LGRS.2008.2000619http://dx.doi.org/10.1109/LGRS.2008.2000619]

Du Q, Zhu W, Yang H and Fowler J E. 2009. Segmented principal component analysis for parallel compression of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 6(4): 713-717 [DOI: 10.1109/LGRS.2009.2024175http://dx.doi.org/10.1109/LGRS.2009.2024175]

Falco N, Benediktsson J A and Bruzzone L. 2014. A study on the effectiveness of different independent component analysis algorithms for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2183-2199 [DOI: 10.1109/JSTARS.2014.2329792http://dx.doi.org/10.1109/JSTARS.2014.2329792]

Fang H and Liang S. 2008. Leaf area index models//Encyclopedia of Ecology. Oxford: Academic Press: 2139-2148 [DOI: 10.1016/B978-0-12-409548-9.09076-Xhttp://dx.doi.org/10.1016/B978-0-12-409548-9.09076-X]

Fang Y, Li H, Ma Y, Liang K, Hu Y J, Zhang S J and Wang H Y. 2014. Dimensionality reduction of hyperspectral images based on robust spatial information using locally linear embedding. IEEE Geoscience and Remote Sensing Letters, 11(10): 1712-1716 [DOI: 10.1109/LGRS.2014.2306689http://dx.doi.org/10.1109/LGRS.2014.2306689]

Fassnacht F E, Neumann C, Förster M, Buddenbaum H, Ghosh A, Clasen A, Joshi P K and Koch B. 2014. Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2547-2561 [DOI: 10.1109/JSTARS.2014.2329390http://dx.doi.org/10.1109/JSTARS.2014.2329390]

Fauvel M, Tarabalka Y, Benediktsson J A, Chanussot J and Tilton J C. 2013. Advances in spectral-spatial classification of hyperspectral images. Proceedings of the IEEE, 101(3): 652-675 [DOI: 10.1109/JPROC.2012.2197589http://dx.doi.org/10.1109/JPROC.2012.2197589]

Feng J, Jiao L C, Liu F, Sun T and Zhang X R. 2015. Mutual-information-based semi-supervised hyperspectral band selection with high discrimination, high information, and low redundancy. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2956-2969 [DOI: 10.1109/TGRS.2014.2367022http://dx.doi.org/10.1109/TGRS.2014.2367022]

Filippi A M and Jensen J R. 2007. Effect of continuum removal on hyperspectral coastal vegetation classification using a fuzzy learning vector quantizer. IEEE Transactions on Geoscience and Remote Sensing, 45(6): 1857-1869 [DOI: 10.1109/TGRS.2007.894929http://dx.doi.org/10.1109/TGRS.2007.894929]

Gan F P, Xiong S Q, Wang R S, Yan B K, Liu S W, Yao G Q, Zhang Z G, Zhou Q, Yang S M and Wang Q H. 2014. Hyperspectral Mineral Mapping and Applications. Beijing: Science Press

甘甫平, 熊盛青, 王润生, 闫柏棍, 刘圣伟, 姚国清, 张宗贵, 周强, 杨苏明, 王青华. 2014. 高光谱矿物填图及示范应用. 北京: 科学出版社

Gao B C. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266 [DOI: 10.1016/S0034-4257(96)00067-3http://dx.doi.org/10.1016/S0034-4257(96)00067-3]

Ghamisi P, Benediktsson J A and Sveinsson J R. 2014. Automatic spectral–spatial classification framework based on attribute profiles and supervised feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5771-5782 [DOI: 10.1109/TGRS.2013.2292544http://dx.doi.org/10.1109/TGRS.2013.2292544]

Gong M G, Zhang M Y and Yuan Y. 2016. Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 54(1): 544-557 [DOI: 10.1109/TGRS.2015.2461653http://dx.doi.org/10.1109/TGRS.2015.2461653]

Gormus E T, Canagarajah N and Achim A. 2012. Dimensionality reduction of hyperspectral images using empirical mode decompositions and wavelets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(6): 1821-1830 [DOI: 10.1109/JSTARS.2012.2203587http://dx.doi.org/10.1109/JSTARS.2012.2203587]

Groves P and Bajcsy P. 2003. Methodology for hyperspectral band and classification model selection//IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data. Greenbelt, MD: IEEE: 120-128 [DOI: 10.1109/WARSD.2003.1295183http://dx.doi.org/10.1109/WARSD.2003.1295183]

Gu Y F, Liu T Z, Jia X P, Benediktsson J A and Chanussot J. 2016. Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 54(6): 3235-3247 [DOI: 10.1109/TGRS.2015.2514161http://dx.doi.org/10.1109/TGRS.2015.2514161]

Gu Y F, Liu Y and Zhang Y. 2008. A selective KPCA algorithm based on high-order statistics for anomaly detection in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 5(1): 43-47 [DOI: 10.1109/LGRS.2007.907304http://dx.doi.org/10.1109/LGRS.2007.907304]

Guo B F. 2020. Enriching absorption features for hyperspectral materials identification. Optics Express, 28(3): 4127-4144 [DOI: 10.1364/OE.384580http://dx.doi.org/10.1364/OE.384580]

Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J and Strachan I B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3): 337-352 [DOI: 10.1016/j.rse.2003.12.013http://dx.doi.org/10.1016/j.rse.2003.12.013]

Hou B, Huang T M and Jiao L C. 2015. Spectral-spatial classification of hyperspectral data using 3-D morphological profile. IEEE Geoscience and Remote Sensing Letters, 12(12): 2364-2368 [DOI: 10.1109/LGRS.2015.2476498http://dx.doi.org/10.1109/LGRS.2015.2476498]

Hsu P H. 2007. Feature extraction of hyperspectral images using wavelet and matching pursuit. ISPRS Journal of Photogrammetry and Remote Sensing, 62(2): 78-92 [DOI: 10.1016/j.isprsjprs.2006.12.004http://dx.doi.org/10.1016/j.isprsjprs.2006.12.004]

Hu P, Liu X B, Cai Y M and Cai Z H. 2019. Band Selection of hyperspectral images using multiobjective optimization-based sparse self-representation. IEEE Geoscience and Remote Sensing Letters, 16(3): 452-456 [DOI: 10.1109/LGRS.2018.2872540http://dx.doi.org/10.1109/LGRS.2018.2872540]

Huang H, Shi G Y, Duan Y L and Zhang L M. 2019. Dimensionality reduction method for hyperspectral images based on weighted spatial-spectral combined preserving embedding. Acta Geodaetica et Cartographica Sinica, 48(8): 1014-1024

黄鸿, 石光耀, 段宇乐, 张丽梅. 2019. 加权空-谱联合保持嵌入的高光谱遥感影像降维方法. 测绘学报, 48(8): 1014-1024 [DOI: 10.11947/j.AGCS.2019.20180229http://dx.doi.org/10.11947/j.AGCS.2019.20180229]

Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309 [DOI: 10.1016/0034-4257(88)90106-Xhttp://dx.doi.org/10.1016/0034-4257(88)90106-X]

Hughes G. 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14(1): 55-63 [DOI: 10.1109/TIT.1968.1054102http://dx.doi.org/10.1109/TIT.1968.1054102]

Ifarraguerri A and Chang C I. 2000. Unsupervised hyperspectral image analysis with projection pursuit. IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2529-2538 [DOI: 10.1109/36.885200http://dx.doi.org/10.1109/36.885200]

Ifarraguerri A and Prairie M W. 2004. Visual method for spectral band selection. IEEE Geoscience and Remote Sensing Letters, 1(2): 101-106 [DOI: 10.1109/LGRS.2003.822879http://dx.doi.org/10.1109/LGRS.2003.822879]

Izquierdo-Verdiguier E, Gómez-Chova L, Bruzzone L and Camps-Valls G. 2014. Semisupervised kernel feature extraction for remote sensing image analysis. IEEE Transactions on Geoscience and Remote Sensing, 52(9): 5567-5578 [DOI: 10.1109/TGRS.2013.2290372http://dx.doi.org/10.1109/TGRS.2013.2290372]

Jia S, Shen L L, Zhu J S and Li Q Q. 2018. A 3-D gabor phase-based coding and matching framework for hyperspectral imagery classification. IEEE Transactions on Cybernetics, 48(4): 1176-1188 [DOI: 10.1109/TCYB.2017.2682846http://dx.doi.org/10.1109/TCYB.2017.2682846]

Jia X P, Kuo B C and Crawford M M. 2013. Feature mining for hyperspectral image classification. Proceedings of the IEEE, 101(3): 676-697 [DOI: 10.1109/JPROC.2012.2229082http://dx.doi.org/10.1109/JPROC.2012.2229082]

Jia X P and Richards J A. 1999. Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Transactions on Geoscience and Remote Sensing, 37(1): 538-542 [DOI: 10.1109/36.739109http://dx.doi.org/10.1109/36.739109]

Kuo B C and Landgrebe D A. 2004. Nonparametric weighted feature extraction for classification. IEEE Transactions on Geoscience and Remote Sensing, 42(5): 1096-1105 [DOI: 10.1109/TGRS.2004.825578http://dx.doi.org/10.1109/TGRS.2004.825578]

Kuo B C, Li C H and Yang J M. 2009. Kernel nonparametric weighted feature extraction for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 47(4): 1139-1155 [DOI: 10.1109/TGRS.2008.2008308http://dx.doi.org/10.1109/TGRS.2008.2008308]

Landgrebe D. 1999. Information extraction principles and methods for multispectral and hyperspectral image data//Information Processing for Remote Sensing. New Jersey: The world Scientific Publishing: 3-37 [DOI: 10.1142/9789812815705_0001http://dx.doi.org/10.1142/9789812815705_0001]

Lee J A and Verleysen M. 2009. Quality assessment of dimensionality reduction: rank-based criteria. Neurocomputing, 72(7/9): 1431-1443 [DOI: 10.1016/j.neucom.2008.12.017http://dx.doi.org/10.1016/j.neucom.2008.12.017]

Li J, Bioucas-Dias J M and Plaza A. 2012. Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 50(3): 809-823 [DOI: 10.1109/TGRS.2011.2162649http://dx.doi.org/10.1109/TGRS.2011.2162649]

Li J, Huang X, Gamba P, Bioucas-Dias J M, Zhang L P, Benediktsson J A and Plaza A. 2015a. Multiple feature learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1592-1606 [DOI: 10.1109/TGRS.2014.2345739http://dx.doi.org/10.1109/TGRS.2014.2345739]

Li W, Chen C, Su H J and Du Q. 2015b. Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 53(7): 3681-3693 [DOI: 10.1109/TGRS.2014.2381602http://dx.doi.org/10.1109/TGRS.2014.2381602]

Li W and Du Q. 2014. Gabor-filtering-based nearest regularized subspace for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1012-1022 [DOI: 10.1109/JSTARS.2013.2295313http://dx.doi.org/10.1109/JSTARS.2013.2295313]

Li W, Prasad S, Fowler J E and Bruce L M. 2011. Locality-preserving discriminant analysis in kernel-induced feature spaces for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 8(5): 894-898 [DOI: 10.1109/ LGRS.2011.2128854http://dx.doi.org/10.1109/LGRS.2011.2128854]

Li X, Ding M L and Pižurica A. 2020. Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(4): 2615-2629 [DOI: 10.1109/TGRS.2019.2952758http://dx.doi.org/10.1109/TGRS.2019.2952758]

Liao W Z, Pizurica A, Scheunders P, Philips W and Pi Y G. 2013. Semisupervised local discriminant analysis for feature extraction in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 184-198 [DOI: 10.1109/TGRS.2012.2200106http://dx.doi.org/10.1109/TGRS.2012.2200106]

Liu D W, Wang W, Wang X K, Wang C, Pei J Y and Chen W C. 2020a. Poststack seismic data denoising based on 3-D convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1598-1629 [DOI: 10.1109/TGRS.2019.2947149http://dx.doi.org/10.1109/TGRS.2019.2947149]

Liu H J, Su H J and Zhao B. 2018. Hyper-spectral multiple features optimization using improved firefly algorithm. Remote Sensing Technology and Application, 33(1): 110-118

刘慧珺, 苏红军, 赵波. 2018. 基于改进萤火虫算法的高光谱遥感多特征优化方法. 遥感技术与应用, 33(1): 110-118 [DOI: 10.11873/j.issn.1004-0323.2018.1.0110http://dx.doi.org/10.11873/j.issn.1004-0323.2018.1.0110]

Liu L, Wang Y B, Peng J H, Zhang L Q, Zhang B and Cao Y. 2020b. Latent relationship guided stacked sparse autoencoder for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3711-3725 [DOI: 10.1109/TGRS.2019.2961564http://dx.doi.org/10.1109/TGRS.2019.2961564]

Lunga D and Ersoy O. 2013. Spherical stochastic neighbor embedding of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 51(2): 857-871 [DOI: 10.1109/TGRS.2012.2205004http://dx.doi.org/10.1109/TGRS.2012.2205004]

Luo F L, Huang H, Ma Z Z and Liu J M. 2016. Semisupervised sparse manifold discriminative analysis for feature extraction of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 6197-6211 [DOI: 10.1109/TGRS.2016.2583219http://dx.doi.org/10.1109/TGRS.2016.2583219]

Martínez-Usómartinez-Uso A, Pla F, Sotoca J M and García-Sevilla P. 2007. Clustering-based hyperspectral band selection using information measures. IEEE Transactions on Geoscience and Remote Sensing, 45(12): 4158-4171 [DOI: 10.1109/TGRS.2007.904951http://dx.doi.org/10.1109/TGRS.2007.904951]

Matteoli S, Veracini T, Diani M and Corsini G. 2014. Background density nonparametric estimation with data-adaptive bandwidths for the detection of anomalies in multi-hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 11(1): 163-167 [DOI: 10.1109/LGRS.2013.2250907http://dx.doi.org/10.1109/LGRS.2013.2250907]

Mura M D, Villa A, Benediktsson J A, Chanussot J and Bruzzone L. 2011. Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geoscience and Remote Sensing Letters, 8(3): 542-546 [DOI: 10.1109/LGRS.2010.2091253http://dx.doi.org/10.1109/LGRS.2010.2091253]

Ni D and Ma H B. 2015. Hyperspectral image classification via sparse code histogram. IEEE Geoscience and Remote Sensing Letters, 12(9): 1843-1847 [DOI: 10.1109/LGRS.2015.2430871http://dx.doi.org/10.1109/LGRS.2015.2430871]

Nie F P, Xiang S M, Song Y Q and Zhang C S. 2009. Extracting the optimal dimensionality for local tensor discriminant analysis. Pattern Recognition, 42(1): 105-114 [DOI: 10.1016/j.patcog.2008.03.012http://dx.doi.org/10.1016/j.patcog.2008.03.012]

Pan B, Shi Z W and Xu X. 2019. Analysis for the weakly pareto optimum in multiobjective-based hyperspectral band selection. IEEE Transactions on Geoscience and Remote Sensing, 57(6): 3729-3740 [DOI: 10.1109/TGRS.2018.2886853http://dx.doi.org/10.1109/TGRS.2018.2886853]

Paoli A, Melgani F and Pasolli E. 2009. Clustering of hyperspectral images based on multiobjective particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing, 47(12): 4175-4188 [DOI: 10.1109/TGRS.2009.2023666http://dx.doi.org/10.1109/TGRS.2009.2023666]

Patro R N, Subudhi S, Biswal P K and Dell'Acqua F. 2021. A review on unsupervised band selection techniques: Land cover classification for hyperspectral earth observation data. IEEE Geoscience and Remote Sensing Magazine, 9(3): 72-111 [DOI: 10.1109/MGRS.2021.3051979http://dx.doi.org/10.1109/MGRS.2021.3051979]

Piech M A and Piech K R. 1987. Symbolic representation of hyperspectral data. Applied Optics, 26(18): 4018-4026 [DOI: 10.1364/AO.26.004018http://dx.doi.org/10.1364/AO.26.004018]

Plaza A, Martinez P, Perez R and Plaza J. 2004. A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 650-663 [DOI: 10.1109/TGRS.2003.820314http://dx.doi.org/10.1109/TGRS.2003.820314]

Plaza A, Martinez P, Plaza J and Perez R. 2005. Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, 43(3): 466-479 [DOI: 10.1109/TGRS.2004.841417http://dx.doi.org/10.1109/TGRS.2004.841417]

Pu R L and Gong P. 2000. Hyperspectral Remote Sensing and its Applications. Beijing: Higher Education Press

浦瑞良, 宫鹏. 2000. 高光谱遥感及其应用. 北京: 高等教育出版社

Ren Y M, Liao L, Maybank S J, Zhang Y N and Liu X. 2017. Hyperspectral image spectral-spatial feature extraction via tensor principal component analysis. IEEE Geoscience and Remote Sensing Letters, 14(9): 1431-1435 [DOI: 10.1109/LGRS.2017.2686878http://dx.doi.org/10.1109/LGRS.2017.2686878]

Richards J A and Jia X P. 2006. Remote Sensing Digital Image Analysis. 4th ed. Berlin Heidelberg: Springer-Verlag [DOI: 10.1007/3-540-29711-1http://dx.doi.org/10.1007/3-540-29711-1]

Roy S K, Das S, Song T C and Chanda B. 2021. DARecNet-BS: unsupervised dual-attention reconstruction network for hyperspectral band selection. IEEE Geoscience and Remote Sensing Letters, 18(12): 2152-2156 [DOI: 10.1109/LGRS.2020.3013235http://dx.doi.org/10.1109/LGRS.2020.3013235]

Serpico S B and Moser G. 2007. Extraction of spectral channels from hyperspectral images for classification purposes. IEEE Transactions on Geoscience and Remote Sensing, 45(2): 484-495 [DOI: 10.1109/TGRS.2006.886177http://dx.doi.org/10.1109/TGRS.2006.886177]

Shen H, Jegelka S and Gretton A. 2009. Fast kernel-based independent component analysis. IEEE Transactions on Signal Processing, 57(9): 3498-3511 [DOI: 10.1109/TSP.2009.2022857http://dx.doi.org/10.1109/TSP.2009.2022857]

Somol P, Pudil P and Kittler J. 2004. Fast branch and bound algorithms for optimal feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(7): 900-912 [DOI: 10.1109/TPAMI.2004.28http://dx.doi.org/10.1109/TPAMI.2004.28]

Song M P, Shang X D, Wang Y L, Yu C Y and Chang C I. 2019. Class information-based band selection for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 8394-8416 [DOI: 10.1109/TGRS.2019.2920891http://dx.doi.org/10.1109/TGRS.2019.2920891]

Song X R, Zou L and Wu L D. 2021. Detection of subpixel targets on hyperspectral remote sensing imagery based on background endmember extraction. IEEE Transactions on Geoscience and Remote Sensing, 59(3): 2365-2377 [DOI: 10.1109/TGRS.2020.3002461http://dx.doi.org/10.1109/TGRS.2020.3002461]

Su H J and Du Q. 2012. Hyperspectral band clustering and band selection for urban land cover classification. Geocarto International, 27(5): 395-411 [DOI: 10.1080/10106049.2011.643322http://dx.doi.org/10.1080/10106049.2011.643322]

Su H J, Du Q, Chen G S and Du P J. 2014. Optimized hyperspectral band selection using particle swarm optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2659-2670 [DOI: 10.1109/JSTARS.2014.2312539http://dx.doi.org/10.1109/JSTARS.2014.2312539]

Su H J, Sheng Y H, Yang H and Du Q. 2011. Orthogonal projection divergence-based hyperspectral band selection. Spectroscopy and Spectral Analysis, 31(5): 1309-1313

苏红军, 盛业华, Yang H, Du Q. 2011. 基于正交投影散度的高光谱遥感波段选择算法. 光谱学与光谱分析, 31(5): 1309-1313 [DOI: 10.3964/jissn1000-0593(2011)05-1309-05http://dx.doi.org/10.3964/jissn1000-0593(2011)05-1309-05]

Su H J, Yang H, Du Q and Sheng Y H. 2011. Semisupervised band clustering for dimensionality reduction of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 8(6): 1135-1139 [DOI: 10.1109/LGRS.2011.2158185http://dx.doi.org/10.1109/LGRS.2011.2158185]

Su H J, Yong B and Du Q. 2016. Hyperspectral band selection using improved firefly algorithm. IEEE Geoscience and Remote Sensing Letters, 13(1): 68-72 [DOI: 10.1109/LGRS.2015.2497085http://dx.doi.org/10.1109/LGRS.2015.2497085]

Sun K, Geng X R, Ji L Y and Lu Y. 2014. A new band selection method for hyperspectral image based on data quality. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2697-2703 [DOI: 10.1109/JSTARS.2014.2320299http://dx.doi.org/10.1109/JSTARS.2014.2320299]

Sun W W, Liu C, Shi B Q and Li W Y. 2013. Random matrix-based nonnegative sparse representation for hyperspectral image classification. Journal of Tongji University(Natural Science), 41(8): 1274-1280

孙伟伟, 刘春, 施蓓琦, 李巍岳. 2013. 基于随机矩阵的高光谱影像非负稀疏表达分类. 同济大学学报(自然科学版), 41(8): 1274-1280 [DOI: 10.3969/j.issn.0253-374x.2013.08.026http://dx.doi.org/10.3969/j.issn.0253-374x.2013.08.026]

Sun W W, Peng J T, Yang G and Du Q. 2020. Correntropy-based sparse spectral clustering for hyperspectral band selection. IEEE Geoscience and Remote Sensing Letters, 17(3): 484-488 [DOI: 10.1109/LGRS.2019.2924934http://dx.doi.org/10.1109/LGRS.2019.2924934]

Sun W W, Zhang L P, Du B, Li W Y and Mark Lai Y. 2015. Band selection using improved sparse subspace clustering for hyperspectral imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2784-2797 [DOI: 10.1109/ JSTARS.2015.2417156http://dx.doi.org/10.1109/JSTARS.2015.2417156]

Sunshine J M, Pieters C M and Pratt S F. 1990. Deconvolution of mineral absorption bands: an improved approach. Journal of Geophysical Research: Solid Earth, 95(B5): 6955-6966 [DOI: 10.1029/JB095iB05p06955http://dx.doi.org/10.1029/JB095iB05p06955]

Tan K, Wu F Y, Du Q, Du P J and Chen Y. 2019. A parallel Gaussian-Bernoulli restricted Boltzmann machine for mining area classification with hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(2): 627-636 [DOI: 10.1109/JSTARS.2019.2892975http://dx.doi.org/10.1109/JSTARS.2019.2892975]

Tao C, Pan H B, Li Y S and Zou Z R. 2015. Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification. IEEE Geoscience and Remote Sensing Letters, 12(12): 2438-2442 [DOI: 10.1109/LGRS.2015.2482520http://dx.doi.org/10.1109/LGRS.2015.2482520]

Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing: the Principle, Technology and Application. Beijing: Higher Education Press

童庆禧, 张兵, 郑兰芬. 2006. 高光谱遥感: 原理、技术与应用. 北京: 高等教育出版社

Tyo J S, Konsolakis A, Diersen D I and Olsen R C. 2003. Principal-components-based display strategy for spectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 41(3): 708-718 [DOI: 10.1109/TGRS.2003.808879http://dx.doi.org/10.1109/TGRS.2003.808879]

Van der Meer F. 2004. Analysis of spectral absorption features in hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 5(1): 55-68 [DOI: 10.1016/j.jag.2003.09.001http://dx.doi.org/10.1016/j.jag.2003.09.001]

Velasco-Forero S and Angulo J. 2013. Classification of Hyperspectral Images by Tensor Modeling and Additive Morphological Decomposition. Pattern Recognition, 46(2): 566-577 [DOI: 10.1016/j.patcog.2012.08.011http://dx.doi.org/10.1016/j.patcog.2012.08.011]

Wang J and Chang C I. 2006. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1586-1600 [DOI: 10.1109/TGRS.2005.863297http://dx.doi.org/10.1109/TGRS.2005.863297]

Wang J L, Hou B, Jiao L C and Wang S. 2020. POL-SAR image classification based on modified stacked autoencoder network and data distribution. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 1678-1695 [DOI: 10.1109/TGRS.2019.2947633http://dx.doi.org/10.1109/TGRS.2019.2947633]

Wang J N, Zheng L F and Tong Q X. 1996. The spectral absorption identification model and mineral mapping by imaging spectrometer data. Remote Sensing of Environment China, 11(1): 20-31

王晋年, 郑兰芬, 童庆禧. 1996. 成象光谱图象光谱吸收鉴别模型与矿物填图研究. 环境遥感, 11(1): 20-31

Wang J X, Ye M C, Xiong F C and Qian Y T. 2021. Cross-scene hyperspectral feature selection via hybrid whale optimization algorithm with simulated annealing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 2473-2483 [DOI: 10.1109/JSTARS.2021.3056593http://dx.doi.org/10.1109/JSTARS.2021.3056593]

Wang Q, Yuan Z H, Du Q and Li X L. 2019. GETNET: a general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 3-13 [DOI: 10.1109/TGRS.2018.2849692http://dx.doi.org/10.1109/TGRS.2018.2849692]

Wu C C, Chu S and Chang C I. 2008. Sequential N-FINDR algorithms//Proceedings Volume 7086, Imaging Spectrometry XIII. San Diego, CA: SPIE: 106-117 [DOI: 10.1117/12.795262http://dx.doi.org/10.1117/12.795262]

Xia J S, Chanussot J, Du P J and He X Y. 2015a. Spectral-spatial classification for hyperspectral data using rotation forests with local feature extraction and Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2532-2546 [DOI: 10.1109/TGRS.2014.2361618http://dx.doi.org/10.1109/TGRS.2014.2361618]

Xia J S, Falco N, Benediktsson J A, Du P J and Chanussot J. 2017. Hyperspectral image classification with rotation random forest via KPCA. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4): 1601-1609 [DOI: 10.1109/JSTARS.2016.2636877http://dx.doi.org/10.1109/JSTARS.2016.2636877]

Xia J S, Mura M D, Chanussot J, Du P J and He X Y. 2015b. Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 4768-4786 [DOI: 10.1109/ TGRS.2015.2409195http://dx.doi.org/10.1109/TGRS.2015.2409195]

Xie W Y, Li Y S, Lei J, Yang J, Chang C I and Li Z. 2020. Hyperspectral band selection for spectral–spatial anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3426-3436 [DOI: 10.1109/TGRS.2019.2956159http://dx.doi.org/10.1109/TGRS.2019.2956159]

Xu H Q. 2013. A remote sensing urban ecological index and its application. Acta Ecologica Sinica, 33(24): 7853-7862

徐涵秋. 2013. 城市遥感生态指数的创建及其应用. 生态学报, 33(24): 7853-7862 [DOI: 10.5846/stxb201208301223http://dx.doi.org/10.5846/stxb201208301223]

Xue B, Zhang M J and Browne W N. 2013. Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Transactions on Cybernetics, 43(6): 1656-1671 [DOI: 10.1109/TSMCB.2012.2227469http://dx.doi.org/10.1109/TSMCB.2012.2227469]

Xue Z H. 2015. Hyperspectral Remote Sensing Image Classification via Sparse Graph Embedding. Nanjing: Nanjing University

薛朝辉. 2015. 高光谱遥感影像稀疏图嵌入分类研究. 南京: 南京大学

Yang H, Du Q and Chen G S. 2012. Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2): 544-554 [DOI: 10.1109/JSTARS.2012.2185822http://dx.doi.org/10.1109/JSTARS.2012.2185822]

Yang H, Du Q, Su H J and Sheng Y H. 2011. An efficient method for supervised hyperspectral band selection. IEEE Geoscience and Remote Sensing Letters, 8(1): 138-142 [DOI: 10.1109/LGRS.2010.2053516http://dx.doi.org/10.1109/LGRS.2010.2053516]

Yang H L and Crawford M M. 2016. Spectral and spatial proximity-based manifold alignment for multitemporal hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 54(1): 51-64 [DOI: 10.1109/TGRS.2015.2449736http://dx.doi.org/10.1109/TGRS.2015.2449736]

Yang J M, Yu P T and Kuo B C. 2010. A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data. IEEE Transactions on Geoscience and Remote Sensing, 48(3): 1279-1293 [DOI: 10.1109/TGRS.2009.2031812http://dx.doi.org/10.1109/TGRS.2009.2031812]

Yao H B and Tian L. 2003. A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1469-1478 [DOI: 10.1109/TGRS.2003.811691http://dx.doi.org/10.1109/TGRS.2003.811691]

Zare A and Gader P. 2008. Hyperspectral band selection and endmember detection using sparsity promoting priors. IEEE Geoscience and Remote Sensing Letters, 5(2): 256-260 [DOI: 10.1109/LGRS.2008.915934http://dx.doi.org/10.1109/LGRS.2008.915934]

Zha Y, Gao J and Ni S. 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3): 583-594 [DOI: 10.1080/01431160304987http://dx.doi.org/10.1080/01431160304987]

Zhang B. 2011a. Intelligent remote sensing satellite system. Journal of Remote Sensing, 15(3): 415-431

张兵. 2011. 智能遥感卫星系统. 遥感学报, 15(3): 415-431 [DOI: CNKI:SUN:YGXB.0.2011-03-003http://dx.doi.org/CNKI:SUN:YGXB.0.2011-03-003]

Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090

张兵. 2016. 高光谱图像处理与信息提取前沿. 遥感学报, 20(5): 1062-1090 [DOI: 10.11834/jrs.20166179http://dx.doi.org/10.11834/jrs.20166179]

Zhang B, Chen Z C, Zheng L F, Tong Q X, Liu Y N, Yang Y D and Xue Y Q. 2004. Object detection based on feature extraction from hyperspectral imagery and convex cone projection transform. Journal of Infrared and Millimeter Waves, 23(6): 441-445, 450

张兵, 陈正超, 郑兰芬, 童庆禧, 刘银年, 杨一德, 薛永祺. 2004. 基于高光谱图像特征提取与凸面几何体投影变换的目标探测. 红外与毫米波学报, 23(6): 441-445, 450 [DOI: 10.3321/j.issn:1001-9014.2004.06.010http://dx.doi.org/10.3321/j.issn:1001-9014.2004.06.010]

Zhang B and Gao L R. 2011b. Hyperspectral Image Classification and Target Detection. Beijing: Science Press

张兵, 高连如. 2011. 高光谱图像分类与目标探测. 北京: 科学出版社

Zhang J X, Zhang P, Li B C, Jing L and Lv T L. 2020. Semisupervised feature extraction based on collaborative label propagation for hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 17(11): 1958-1962 [DOI: 10.1109/LGRS.2019.2958410http://dx.doi.org/10.1109/LGRS.2019.2958410]

Zhang L F, Zhang L P, Tao D C and Huang X. 2012. On combining multiple features for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 50(3): 879-893 [DOI: 10.1109/TGRS.2011.2162339http://dx.doi.org/10.1109/TGRS.2011.2162339]

Zhang L F, Zhang Q, Du B, Huang X, Tang Y Y and Tao D C. 2018a. Simultaneous spectral-spatial feature selection and extraction for hyperspectral images. IEEE Transactions on Cybernetics, 48(1): 16-28 [DOI: 10.1109/TCYB.2016.2605044http://dx.doi.org/10.1109/TCYB.2016.2605044]

Zhang L P and Zhang L F. 2011. Hyperspectral Remote Sensing. Beijing: China Surveying and Mapping Press

张良培, 张立福. 2011. 高光谱遥感. 北京: 测绘出版社

Zhang L P, Zhang L F, Tao D C and Huang X. 2013. Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 242-256 [DOI: 10.1109/TGRS.2012.2197860http://dx.doi.org/10.1109/TGRS.2012.2197860]

Zhang L P, Zhong Y F, Huang B, Gong J Y and Li P X. 2007. Dimensionality reduction based on clonal selection for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 45(12): 4172-4186 [DOI: 10.1109/TGRS.2007.905311http://dx.doi.org/10.1109/TGRS.2007.905311]

Zhang X R, Gao Z Y, Jiao L C and Zhou H Y. 2018b. Multifeature hyperspectral image classification with local and nonlocal spatial information via Markov random field in semantic space. IEEE Transactions on Geoscience and Remote Sensing, 56(3): 1409-1424 [DOI: 10.1109/TGRS.2017.2762593http://dx.doi.org/10.1109/TGRS.2017.2762593]

Zhang Y H and Prasad S. 2015. Locality preserving composite kernel feature extraction for multi-source geospatial image analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3): 1385-1392 [DOI: 10.1109/JSTARS.2014.2348537http://dx.doi.org/10.1109/JSTARS.2014.2348537]

Zhong Z S, Fan B, Duan J Y, Wang L F, Ding K, Xiang S M and Pan C H. 2015. Discriminant tensor spectral-spatial feature extraction for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 12(5): 1028-1032 [DOI: 10.1109/LGRS.2014.2375188http://dx.doi.org/10.1109/LGRS.2014.2375188]

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