Research Progress Review of Hyperspectral Remote Sensing Image Band Selection

doi:10.3778/j.issn.1002-8331.2111-0403

Abstract

Abstract: Hyperspectral imaging remote sensing can obtain abundant spectral, radiation and spatial information of ground objects, which has been widely used in various fields of national economy. But its narrow band spacing brings not only rich spectral information, but also information redundancy and the difficulty of data processing. Therefore, before the practical application of hyperspectral remote sensing data, band selection is needed to extract spectral features and reduce the data dimension. This review summarizes the research progress of band selection for hyperspectral remote sensing images. Based on the analysis and summary of band selection strategies, the related technology and the latest research status are expounded from six aspects：the evaluation criteria of band selection, the band selection based on the combination of spatial and spectral features, the band selection based on semi-supervised learning, the band selection based on sparse representation, the band selection based on intelligent search and the band selection based on deep learning. Then, the current problems and challenges faced by hyperspectral image band selection are discussed. Finally, the future development direction of hyperspectral image band selection is predicted.

Key words: digital image processing, remote sensing, hyperspectral image, band selection

摘要： 高光谱成像遥感技术可获取地物的光谱、辐射和空间信息，在国民经济的各个领域得到广泛的应用。但其狭窄的波段间距带来丰富光谱信息的同时，也带来了信息冗余，增加了数据处理的难度。因此，高光谱遥感数据在进行实际应用前，需要进行波段选择并提取光谱特征，降低数据维数。对高光谱遥感图像的波段选择研究进展进行了综述，在分析、归纳波段选择策略的基础上，从评价准则、空谱特征、半监督学习、稀疏表达、智能搜索和深度学习六方面阐述了波段选择方法的最新研究进展，探讨了当前高光谱图像波段选择面临的问题与挑战，对未来发展的趋势进行了预估。

关键词: 数字图像处理, 遥感, 高光谱图像, 波段选择

YANG Hongyan, DU Jianmin. Research Progress Review of Hyperspectral Remote Sensing Image Band Selection[J]. Computer Engineering and Applications, 2022, 58(10): 1-12.

杨红艳, 杜健民. 高光谱遥感图像波段选择研究进展综述[J]. 计算机工程与应用, 2022, 58(10): 1-12.

References

[1] 童庆禧，张兵，郑芬兰.高光谱遥感——原理、技术与应用[M].北京：高等教育出版社，2006：40-47.
TONG Q X，ZHANG B，ZHENG F L.Hyperspectral remote sensing：the principle，technology and application[M].Beijing：Higher Education Press，2006：40-47.
[2] 浦瑞良，宫鹏.高光谱遥感及其应用[M].北京：高等教育出版社，2000：2-10.
PU R L，GONG P.Hyperspectral remote sensing and its applications[M].Beijing：Higher Education Press，2000：2-10.
[3] 张良培，张立福.高光谱遥感[M].武汉：武汉大学出版社，2005：3-8.
ZHANG L P，ZHANG L F.Hyperspectral remote sensing[M].Wuhan：Wuhan University Press，2005：3-8.
[4] HUGHES G F.On the mean accuracy of statistical pattern recognizers[J].IEEE Transactions on Information Theory，1968，14（1）：55-63.
[5] JIA X P，KUO B C，CRAWFORD M M.Feature mining for hyperspectral image classification[J].Proceedings of the IEEE，2013，101（3）：676-697.
[6] CHANG C I.Hyperspectral imaging：techniques for spectral detection and classification[M].New York：Kluwer Academic/Plenum Publishers，2003：5-15.
[7] BAJCSY P，GROVES P.Methodology for hyperspectral band selection[J].Photogrammetric Engineering and Remote Sensing，2004，70（7）：793-802.
[8] ZHANG H，LAN J H，GUO Y K.A dense spatial-spectral attention network for hyperspectral image band selection[J].Remote Sensing Letters，2021（1）：1-14.
[9] 周佳巧，崔文楠，张涛，等.基于光谱测量数据的自适应波段选择技术[J].激光与光电子学进展，2019，56（23）：232501.
ZHOU J Q，CUI W N，ZHANG T，et al.Adaptive band selection technique based on spectral measurement data[J].Laser & Optoelectronics Progress，2019，56（23）：232501.
[10] SUN W W，DU Q.Hyperspectral band selection：a review[J].IEEE Geoscience and Remote Sensing Magazine，2019，7（2）：118-139.
[11] SU H J，DU Q，CHEN G S，et al.Optimized hyperspectral band selection using particle swarm optimization[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2014，7（6）：2659-2670.
[12] IFARRAGUERRI A，PRAIRIE M W.Visual method for spectral band selection[J].IEEE Geoscience and Remote Sensing Letters，2004，1（2）：101-106.
[13] RICHARDS J A，JIA X P.Remote sensing digital image analysis[M].Berlin：Springer，2006：33-45.
[14] QIAN D，HE Y.Similarity-based unsupervised band selection for hyperspectral image analysis[J].IEEE Geoscience and Remote Sensing Letters，2008，5（4）：564-568.
[15] CHANG C I.An information-theoretic approach to spectral variability，similarity，and discrimination for hyperspectral image analysis[J].IEEE Transactions on Information Theory，2000，46（5）：1927-1932.
[16] KUMAR B，MANOHARAN P.Whale optimization-based band selection technique for hyperspectral image classification[J].International Journal of Remote Sensing，2021，42（13）：5109-5147.
[17] KIM J H，KIM J，YANG Y，et al.Covariance-based band selection and its application to near-real-time hyperspectral target detection[J].Optical Engineering，2017，56（5）：1053101.
[18] CHANG C I，QIAN D，SUN T L，et al.A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing，1999，37（6）：2631-2641.
[19] CHANG C I，SU W.Constrained band selection for hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing，2006，44（6）：1575-1585.
[20] FENG J，JIAO L C，ZHANG X R，et al.Hyperspectral band selection based on trivariate mutual information and clonal selection[J].IEEE Transactions on Geoscience and Remote Sensing，2014，52（7）：4092-4105.
[21] GUO B F，GUNN S R，DAMPER R I，et al.Band selection for hyperspectral image classification using mutual information[J].IEEE Geoscience and Remote Sensing Letters，2006，3（4）：522-526.
[22] SUI C H，LI C，FENG J，et al.Unsupervised manifold-preserving and weakly redundant band selection method for hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing，2020，58（2）：1156-1170.
[23] WANG Q，ZHANG F H，LI X L.Optimal clustering framework for hyperspectral band selection[J].IEEE Transactions on Geoscience and Remote Sensing，2018，56（10）：5910-5922.
[24] MITRA P，MURTHY C A，PAL S K.Unsupervised feature selection using feature similarity[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2002，24（3）：301-312.
[25] VARADE D，MAURYA A K，DIKSHIT O.Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification[J].Geocarto International，2019，36（15）：1-23.
[26] 赵洁.基于K均值聚类的高光谱遥感影像分类研究[J].地理空间信息，2016，14（3）：26-29.
ZHAO J.Research on hyperspectral remote sensing images classification based on K-means clustering[J].Geospatial Information，2016，14（3）：26-29.
[27] KALIDINDI R K，PARDHA S V G，DAVULURI R.Featured clustering and ranking-based bad cluster removal for hyperspectral band selection and classification using ensemble of binary SVM classifiers[J].International Journal of Information Technology Project Management，2021，12（4）：61-78.
[28] 任智伟，吴玲达.基于AP聚类的高光谱波段选择[J].激光与光电子学进展，2018，55（10）：393-397.
REN Z W，WU L D.Hyperspectral band selection based on affinity propagation clustering[J].Laser & Optoelectronics Progress，2018，55（10）：393-397.
[29] SU H J，SHENG Y H，DU P J，et al.Adaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection[J].Applied Optics，2012，51（14）：2656-2663.
[30] MARTINEZ U A，PLA F，SOTOCA J M，et al.Clustering-based hyperspectral band selection using information measures[J].IEEE Transactions on Geoscience and Remote Sensing，2007，45（12）：4158-4171.
[31] SUN W W，PENG J T，YANG G，et al.Fast and latent low-rank subspace clustering for hyperspectral band selection[J].IEEE Transactions on Geoscience and Remote Sensing，2020，58（6）：3906-3915.
[32] PATRA S，MODI P，BRUZZONE L.Hyperspectral band selection based on rough set[J].IEEE Transactions on Geoscience and Remote Sensing，2015，53（10）：5495-5503.
[33] TASKIN G，KAYA H，BRUZZONE L.Feature selection based on high dimensional model representation for hyperspectral images[J].IEEE Transactions on Image Processing，2017，26（6）：2918-2928.
[34] QV H，YIN J H，LUO X Y，et al.Band dual density discrimination analysis for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing，2018，56（12）：7257-7271.
[35] HABERMANN M，FREMONT V，SHIGUEMORI E H.Supervised band selection in hyperspectral images using single-layer neural networks[J].International Journal of Remote Sensing，2019，40（10）：3900-3926.
[36] ZHANG M Y，GONG M G，CHAN Y Q.Hyperspectral band selection based on multi-objective optimization with high information and low redundancy[J].Applied Soft Computing，2018，70：604-621.
[37] BARMAN B，PATRA S.Variable precision rough set based unsupervised band selection technique for hyperspectral image classification[J].Knowledge-Based Systems，2020，193：105414.
[38] 刘瑶.基于邻域粗糙集的高光谱波段选择算法研究[D].哈尔滨：哈尔滨工程大学，2017.
LIU Y.The research on hyperspectral band selection algorithm based on neighborhood rough set[D].Harbin：Harbin Engineering University，2017.
[39] CHAVEZ P S，BERLIN G L，SOWERS L B.Statistical methods for selection Landsat MSS ratios[J].Journal of Applied Photographic Engineering，1982，8：23-30.
[40] 刘春红，赵春晖，张凌雁.一种新的高光谱遥感图像降维方法[J].中国图象图形学报，2005，10（2）：218-222.
LIU C H，ZHAO C H，ZHANG L Y.A new method of hyperspectral remote sensing image dimensional reduction[J].Journal of Image and Graphics，2005，10（2）：218-222.
[41] 周勇兵.高光谱图像波段选择方法探究[J].江西测绘，2016（3）：5-9.
ZHOU Y B.The research on the method of band selection of hyperspectral image[J].Jiangxi Cehui，2016（3）：5-9.
[42] 赵亮，王立国，刘丹凤.高光谱图像子空间的波段选择[J].遥感学报，2019，23（5）：904-910.
ZHAO L，WANG L G，LIU D F.A subspace band selection method for hyperspectral imagery[J].Journal of Remote Sensing，2019，23（5）：904-910.
[43] 邓柳.基于分组波段选择及紧凑字典的高光谱遥感图像分类研究[D].湘潭：湘潭大学，2018.
DENG L.Hyperspectral remote sensing image classification based on grouped band selection and compact dictionary[D].Xiangtan：Xiangtan University，2018.
[44] 苏红军，杜培军，盛业华.高光谱影像波段选择算法研究[J].计算机应用研究，2008，25（4）：1093-1096.
SU H J，DU P J，SHENG Y H.Study on band selection algorithms of hyperspectral image data[J].Application Research of Computers，2008，25（4）：1093-1096.
[45] 吴逍航，付小宁，秦帅.基于信息熵和OIF算法的数据降维[J].计算机科学与应用，2019，9（7）：1365-1376.
WU X H，FU X N，QIN S.Data dimension reduction based on information entropy and OIF algorithms[J].Computer Science and Application，2019，9（7）：1365-1376.
[46] ZHANG Y，HE C L，SONG X F，et al.A multi-strategy integrated multi-objective artificial bee colony for unsupervised band selection of hyperspectral images[J].Swarm and Evolutionary Computation，2021，60（1）：100806.
[47] 张伍，陈红梅.基于核模糊粗糙集的高光谱波段选择算法[J].计算机应用，2020，40（1）：258-263.
ZHANG W，CHEN H M.Hyperspectral band selection algorithm based on kernelized fuzzy rough set[J].Journal of Computer Applications，2020，40（1）：258-263.
[48] FAUVEL M，TARABALKA Y，BENEDIKTSSON J A，et al.Advances in spectral-spatial classification of hyperspectral images[J].Proceedings of the IEEE，2013，101（3）：652-675.
[49] CAO X H，JI Y M，WANG L，et al.Fast hyperspectral band selection based on spatial feature extraction[J].Journal of Real-Time Image Processing，2018，15（3）：555-564.
[50] 梁甜.基于局部空间信息的高光谱波段选择研究[D].西安：西安电子科技大学，2017.
LIANG T.Hyperspectral image band selection based on local spatial information[D].Xi’an：Xidian University，2017.
[51] TAN K，LI E Z，DU Q，et al.Hyperspectral image classification using band selection and morphological profiles[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2014，7（1）：40-48.
[52] FENG J，JIAO L C，LIU F，et al.Mutual-information-based semi-supervised hyperspectral band selection with high discrimination，high information，and low redundancy[J].IEEE Transactions on Geoscience and Remote Sensing，2015，53（5）：2956-2969.
[53] 魏翠翠.基于标记样本扩充的半监督高光谱波段选择方法[D].西安：西安电子科技大学，2019.
WEI C C.Semi-supervised hyperspectral band selection based on labeled sample expansion[D].Xi’an：Xidian University，2019.
[54] CAO X H，JI Y M，LIANG T，et al.A semi-supervised spatially aware wrapper method for hyperspectral band selection[J].International Journal of Remote Sensing，2018，39（12）：4020-4039.
[55] ZHANG Y X，DU B，ZHANG L P.A sparse representation-based binary hypothesis model for target detection in hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing，2015，53（3）：1346-1354.
[56] CHEN Y，NASRABADI N M，TRAN T D.Hyperspectral image classification using dictionary-based sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing，2011，49（10）：3973-3985.
[57] LI S J，QI H R.Sparse representation based band selection for hyperspectral images[C]//2011 18th IEEE International Conference on Image Processing，Brussels，Sep 11-14，2011：2693-2696.
[58] 林健哲.高光谱图像分类及其子问题研究[D].西安：中国科学院大学（中国科学院西安光学精密机械研究所），2016.
LIN J Z.Research on hyperspectral image classification and its sub-problems[D].Xi’an：University of Chinese Academy of Sciences（Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences），2016.
[59] LI J M，QIAN Y T.Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization[J].Journal of Zhejiang University：Science C（Computers & Electronics），2011，12（7）：542-549.
[60] 施蓓琦，刘春，孙伟伟，等.应用稀疏非负矩阵分解聚类实现高光谱影像波段的优化选择[J].测绘学报，2013，42（3）：351-358.
SHI B Q，LIU C，SUN W W，et al.Sparse nonnegative matrix factorization for hyperspectral optimal band selection[J].Acta Geodaetica et Cartographica Sinica，2013，42（3）：351-358.
[61] 孙伟伟，张殿发，杨刚，等.加权概率原型分析的高光谱影像波段选择[J].遥感学报，2018，22（1）：110-118.
SUN W W，ZHANG D F，YANG G，et al.Band selection for hyperspectral imagery based on weighted probabilistic archetypal analysis[J].Journal of Remote Sensing，2018，22（1）：110-118.
[62] 孙伟伟，蒋曼，李巍岳.利用稀疏自表达实现高光谱影像波段选择[J].武汉大学学报（信息科学版），2017，42（4）：441-448.
SUN W W，JIANG M，LI W Y.Band selection using sparse self-representation for hyperspectral imagery[J].Geomatics and Information Science of Wuhan University，2017，42（4）：441-448.
[63] 唐意东，黄树彩，薛爱军.面向目标检测基于稀疏表示的波段选择方法[J].电子学报，2017，45（10）：2368-2374.
TANG Y D，HUANG S C，XUE A J.Sparse representation based band selection for hyperspectral imagery target detection[J].Acta Electronica Sinica，2017，45（10）：2368-2374.
[64] BAISANTRY M，SAO A K，SHUKLA D P.Band selection using combined divergence-correlation index and sparse loadings representation for hyperspectral image classification[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2020，13：5011-5026.
[65] SUN W W，PENG J T，YANG G，et al.Correntropy-based sparse spectral clustering for hyperspectral band selection[J].IEEE Geoscience and Remote Sensing Letters，2020，17（3）：484-488.
[66] FACHAT A，HOFFMANN K H，FRANZ A.Simulated annealing with threshold accepting or Tsallis statistics[J].Computer Physics Communications，2000，132（3）：232-240.
[67] MEDJAHED S A，OUALI M.Band selection based on optimization approach for hyperspectral image classification[J].Egyptian Journal of Remote Sensing and Space Sciences，2018，21：413-418.
[68] RASHEDI E，NEZAMABADI P H，SARYAZDI S.GSA：a gravitational search algorithm[J].Information Sciences，2009，179（13）：2232-2248.
[69] YU L，HAN Y F，MU L L.Improved quantum evolutionary particle swarm optimization for band selection of hyperspectral image[J].Remote Sensing Letters，2020，11（9）：866-875.
[70] 谢欢，陈争光.遗传模拟退火算法在玉米秸秆纤维素含量检测中的应用[J].分析化学，2019，47（12）：1987-1994.
XIE H，CHEN Z G.Application of genetic simulated annealing algorithm in detection of corn straw cellulose[J].Chinese Journal of Analytical Chemistry，2019，47（12）：1987-1994.
[71] 谢欢.基于近红外光谱技术的玉米秸秆主要成分含量检测方法研究[D].大庆：黑龙江八一农垦大学，2020.
XIE H.Study on detection method of main components in corn straw based on near infrared spectroscopy[D].Daqing：Heilongjiang Bayi Agricultural University，2020.
[72] YU S X，BACKER S D，SCHEUNDERS P.Genetic feature selection combined with composite fuzzy nearest neighbor classifiers for hyperspectral satellite imagery[J].Pattern Recognition Letters，2002，23（1/2/3）：183-190.
[73] YAO H B，TIAN L.A genetic-algorithm-based selective principal component analysis（GA-SPCA） method for high-dimensional data feature extraction[J].IEEE Transactions on Geoscience and Remote Sensing，2003，41（6）：1469-1478.
[74] 张明阳.基于进化优化学习的高光谱特征选择与提取[D].西安：西安电子科技大学，2018.
ZHANG M Y.Feature selection and feature extraction for hyperspectral images based on evolutionary optimization and learning[D].Xi’an：Xi’an University of Electronic Science and technology，2018.
[75] 苏乐乐.改进型人工免疫系统及其在高光谱数据处理中的应用[D].北京：中国地质大学，2009.
SU L L.Improved artificial immune system and its application on the process of hyperspectral data[D].Beijing：China University of Geosciences，2009.
[76] 杜倩男.基于进化优化的高光谱特征选择算法研究[D].上海：华东师范大学，2014.
DU Q N.Hyperspectral image feature selection based on evolutionary optimization[D].Shanghai：East China Normal University，2014.
[77] NAGASUBRAMANIAN K，JONES S，SARKAR S，et al.Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems[J].Plant Methods，2018，14（1）：1-13.
[78] SU H J，DU Q，CHEN G S et al.Optimized hyperspectral band selection using particle swarm optimization[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2014，7（6）：2659-2670.
[79] DING X H，LI H P，LI Y，et al.Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image[J].Open Geosciences，2020，12（1）：425-442.
[80] 王偲晗，万幼川，王明威，等.改进蚁群算法及其在高光谱影像分类中的研究[J].计算机工程与应用，2018，54（1）：196-203.
WANG S H，WAN Y C，WANG M W，et al.Improved ant colony optimization algorithm and its research on hyperspectral image classification[J].Computer Engineering and Applications，2018，54（1）：196-203.
[81] WANG M W，WU C M，WANG L Z，et al.A feature selection approach for hyperspectral image based on modified ant lion optimizer[J].Knowledge-Based Systems，2019，168：39-48.
[82] SU H J，YONG B，DU P J，et al.Dynamic classifier selection using spectral-spatial information for hyperspectral image classification[J].Journal of Applied Remote Sensing，2014，8（1）：085095.
[83] HE C L，ZHANG Y，GONG D W.A pseudo-label guided artificial bee colony algorithm for hyperspectral band selection[J].Remote Sensing，2020，12（20）：3456.
[84] XIE F D，LI F F，LEI C K，et al.Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification[J].Applied Soft Computing，2019，75：428-440.
[85] ZHANG Y，HE C L，SONG X F，et al.A multi-strategy integrated multi-objective artificial bee colony for unsupervised band selection of hyperspectral images[J].Swarm and Evolutionary Computation，2021，60（1）：100806.
[86] KARABOGA D，BASTURK B.A powerful and efficient algorithm for numerical function optimization：artificial bee colony（ABC） algorithm[J].Journal of Global Optimization，2007，39（3）：459-471.
[87] SINGH P S，KARTHIKEYAN S.Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm[J].Neural Computing and Applications，2021（7）.
[88] WANG M W，YAN Z Q，LUO J W，et al.A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image[J].Applied Intelligence，2021，51：7766-7780.
[89] 戴天虹，孙春雪，黄建平，等.基于黄金正弦混沌鬣狗算法的高光谱波段选择[J].激光与光电子学进展，2022，59（10）：1030003.
DAI T H，SUN C X，HUANG J P，et al.Hyperspectral band selection based on golden sine and chaotic spotted hyena optimizer algorithm[J].Laser & Optoelectronics Progress，2022，59（10）：1030003.
[90] DOU Z Y，GAO K，ZHANG X D，et al.Band selection of hyperspectral images using attention-based autoencoders[J].IEEE Geoscience and Remote Sensing Letters，2021，18（1）：147-151.
[91] ZHAO L，ZENG Y，LIU P，et al.Band selection with the explanatory gradient saliency maps of convolutional neural networks[J].IEEE Geoscience and Remote Sensing Letters，2020，17（12）：2105-2109.
[92] FENG J，CHEN J T，LIU L G，et al.CNN-based multilayer spatial-spectral feature fusion and sample augmentation with local and nonlocal constraints for hyperspectral image classification[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2019，12（4）：1299-1313.
[93] SAWANT S S，MANOHARAN P，LOGANATHAN A.Band selection strategies for hyperspectral image classification based on machine learning and artificial intelligent techniques—survey[J].Arabian Journal of Geosciences，2021，14（7）：646-656.
[94] SAFARI K，PRASAD S，LABATE D.A multiscale deep learning approach for high-resolution hyperspectral image classification[J].IEEE Geoscience and Remote Sensing Letters，2021，18（1）：167-171.
[95] LIU S J，SHI Q.Multitask deep learning with spectral knowledge for hyperspectral image classification[J].IEEE Geoscience and Remote Sensing Letters，2020，17（12）：2110-2114.
[96] 张祥东，王腾军，朱劭俊，等.基于扩张卷积注意力神经网络的高光谱图像分类[J].光学学报，2021，41（3）：49-59.
ZHANG X D，WANG T J，ZHU S J，et al.Hyperspectral image classification based on dilated convolutional attention neural network[J].Acta Optica Sinica，2021，41（3）：49-59.
[97] HE J，ZHANG C，ZHOU L，et al.Simultaneous determination of five micro-components in chrysanthemum morifolium（Hangbaiju） using near-infrared hyperspectral imaging coupled with deep learning with wavelength selection[J].Infrared Physics and Technology，2021，116：103802.
[98] FENG J，CHEN J T，SUN Q G，et al.Convolutional neural network based on bandwise-independent convolution and hard thresholding for hyperspectral band selection[J].IEEE Transactions on Cybernetics，2020，51（9）：4414-4428.
[99] 陈建通.基于深度神经网络的高光谱波段选择方法研究[D].西安：西安电子科技大学，2020.
CHEN J T.Research on hyperspectral band selection method based on deep neural network[D].Xi’an：Xidian University，2020.
[100] 林洲汉.基于自动编码机的高光谱图像特征提取及分类方法研究[D].哈尔滨：哈尔滨工业大学，2014.
LIN Z H.Hyperspectral image feature extraction and classification based on autoencoders[D].Harbin：Harbin Institute of Technology，2014.
[101] FENG F，WANG S T，WANG C Y，et al.Learning deep hierarchical spatial-spectral features for hyperspectral image classification based on residual 3D-2D CNN[J].Sensors，2019，19（23）：5276-5296.
[102] PAUL A，CHAKI N.Supervised data-driven approach for hyperspectral band selection using quantization[J].Geocarto International，2020，9：1-11.
[103] CHANG C I.Hyperspectral data processing：algorithm design and analysis[M].Hoboken：Wiley，2013：613-663.
[104] 田明华.基于广义光谱角的高光谱波段选择及端元提取方法研究[D].哈尔滨：哈尔滨工程大学，2016.
TIAN M H.Hyperspectral band selection and endmember extraction based on n-dimensional solid spectral angle[D].Harbin：Harbin Engineering University，2016.