高光谱遥感图像亚像元信息提取方法综述

doi:10.11947/j.AGCS.2023.20220491

摘要/Abstract

摘要： 高光谱遥感图像光谱分辨率高、波谱连续、图谱合一，这为精细地物分类、探测和识别提供了数据基础。然而，由于高光谱遥感图像空间分辨率的局限性及地物场景的复杂分布，混合像元普遍存在于高光谱遥感图像。混合像元是高光谱遥感图像精细信息提取与分析中的难点。解决混合像元问题，实现亚像元级信息的提取与分析是近年来高光谱遥感图像解译的热点和前沿。本文系统梳理了高光谱遥感图像亚像元信息提取的主要研究内容，具体从混合像元分解、亚像元制图及亚像元目标探测3个研究方向综述了经典方法，并对国内外相关方向的研究进展、发展前沿及主要挑战进行了分析与评价，最后分析讨论了高光谱遥感图像亚像元信息提取研究在模型构建、优化求解及与应用结合等方面的研究趋势及方向。

关键词: 高光谱遥感, 混合像元, 光谱分解, 亚像元制图, 亚像元目标探测

Abstract: Hyperspectral remote sensing image provides abundant data for precise landcover classification, target detection and object recognition, attributed to its unique advantages in very high spectral resolution, continuous spectum as well as the synchronous acquisition of both image and spectra of objects. Due to the limitation of the spatial resolution and the complicated scenes, mixed pixels are common in hyperspectral remote sensing images. The mixed pixel problem hinders the information extraction and analysis, and consequently, greatly weaks the potential application in various fields. It has become an important frontier scientific issue and hot spot to tackle the mixed pixel problem and realize the information extraction and analysis deep into subpixel scale for hyperspectral remote sensing imagery. This review generates a systematic summary for hyperspectral remote sensing image subpixel information extraction, and conducts a comprehensive review of the classical approaches from three aspects, namely, hyperspectral unmixing, subpixel mapping and subpixel target detection. Additionaly, this paper also analyzes and evaluates the current progress, development frontier and main challenges in related fields at home and abroad. Finally, the research trends and directions are discussed, especially in the aspects of model construction, optimization algorithm, and theoretical research and practical application combination.

Key words: hyperspectral remote sensing, mixed pixel, spectral unmixing, subpixel mapping, subpixel target detection

中图分类号:

P237

冯如意, 王力哲, 曾铁勇. 高光谱遥感图像亚像元信息提取方法综述[J]. 测绘学报, 2023, 52(7): 1187-1201.

FENG Ruyi, WANG Lizhe, ZENG Tieyong. Review of hyperspectral remote sensing image subpixel information extraction[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1187-1201.

参考文献

[1] 童庆禧,张兵,张立福. 中国高光谱遥感的前沿进展[J]. 遥感学报, 2016, 20(5):689-707. TONG Qingxi, ZHANG Bing, ZHANG Lifu. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 2016, 20(5):689-707.
[2] 张良培,李家艺.高光谱图像稀疏信息处理综述与展望[J]. 遥感学报, 2016, 20(5):1091-1101. ZHANG Liangpei, LI Jiayi. Development and prospect of sparse representation-based hyperspectral image processing and analysis[J]. Journal of Remote Sensing, 2016, 20(5):1091-1101.
[3] 杜培军,夏俊士,薛朝辉,等. 高光谱遥感影像分类研究进展[J]. 遥感学报, 2016, 20(2):236-256. DU Peijun, XIA Junshi, XUE Zhaohui, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2):236-256.
[4] 甘甫平,王润生. 高光谱遥感技术在地质领域中的应用[J]. 国土资源遥感,2007, 19(4):57-60. GAN Fuping, WANG Runsheng. The application of the hyperspectral imaging technique to geological investigation[J]. Remote Sensing for Land & Resources, 2007, 19(4):57-60.
[5] 申广荣,王人潮. 植被高光谱遥感的应用研究综述[J]. 上海交通大学学报(农业科学版), 2001, 19(4):315-321. SHEN Guangrong, WANG Renchao. Review of the application of vegetation hyperspectral remote sensing[J]. Journal of Shanghai Jiaotong University(Agriculture Science), 2001, 19(4):315-321.
[6] 刘照欣,赵辽英,厉小润,等. 高光谱亚像元定位的线特征探测法[J]. 测绘学报,2019, 48(11):1464-1474.DOI:10.11947/j.AGCS.2019.20180221. LIU Zhaoxin, ZHAO Liaoying, LI Xiaorun, et al. Linear feature detection for hyperspectral subpixel mapping[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11):1464-1474.DOI:10.11947/j.AGCS.2019.20180221.
[7] BIOUCAS D J M, PLAZA A, CAMPS V G, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Senisng Magazine, 2013,1(2):6-36.
[8] BIOUCAS D J M, PLAZA A, DOBIGEON N, et al. Hyperspectral unmixing overview:geometrical, statistical, and sparse regression-based approaches[J]. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2012,5(2):354-379.
[9] ATKINSON P M. Mapping subpixel boundaries from remotely sensely images[J]. Innovations in GIS, 1997, 4:166-180.
[10] 任武,葛咏. 遥感影像亚像元制图方法研究进展综述[J]. 遥感技术与应用,2011, 26(1):33-44. REN Wu, GE Yong. Progress on subpixel mapping methods for remotely sensed images[J]. Remote Sensing Technology and Application, 2011, 26(1):33-44.
[11] 凌峰,吴胜军,肖飞,等. 遥感影像亚像元定位研究综述[J]. 中国图象图形学报,2011, 16(8):1335-1345. LING Feng, WU Shengjun, XIAO Fei, et al. Subpixel mapping of remotely sensed imagery:a review[J]. Journal of Image and Graphics, 2011, 16(8):1335-1345.
[12] 王群明. 遥感图像亚像元定位及相关技术研究[D]. 哈尔滨:哈尔滨工程大学, 2012. WANG Qunming. Research on subpixel mapping and its related techniques for remote sensing imagery[D]. Harbin:Harbin Engineering University, 2012.
[13] XU Xiong, ZHONG Yanfei, ZHANG Liangpei, et al. Subpixel mapping based on a MAP model with multiple shifted hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):580-593.
[14] SONG Mi, ZHONG Yanfei, MA Ailong, et al. Multiobjective sparse subpixel mapping for remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7):4490-4508.
[15] LING Feng, DU Yun, ZHANG Yihang, et al. Burned-area mapping at the subpixel scale with MODIS images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9):1963-1967.
[16] LI Xiaodong, DU Yun, LING Feng. Sub-pixel-scale land cover map updating by integrating change detection and sub-pixel mapping[J].Photogrammetric Engineering and Remote Sensing, 2015, 81(1):59-67.
[17] ZHONG Yanfei, HE Da, LUO Bin, et al. Contemporary liquid brine exploration on Mars:from spectral unmixing to subpixel mapping[J]. Earth and Space Science, 2019, 6(3):433-466.
[18] 张玉香. 基于高光谱遥感影像稀疏性的小目标探测研究[D]. 武汉:武汉大学, 2016. ZHANG Yuxiang. Sparsity based target detection for hyperspectral remote sensing imagery[D]. Wuhan:Wuhan University, 2016.
[19] 张良培. 高光谱目标探测的进展与前沿问题[J]. 武汉大学学报(信息科学版),2014, 39(12):1387-1400. ZHANG Liangpei. Advance and future challenges in hyperspectral target detection[J]. Geomatics and Information Science of Wuhan University, 2014, 39(12):1387-1400.
[20] 耿修瑞,赵永超. 高光谱遥感图像小目标探测的基本原理[J]. 中国科学(D辑:地球科学), 2007, 37(8):1081-1087. GENG Xiurui, ZHAO Yongchao. Basic principles of target detection in hyperspectral remote sensing imagery[J]. Science in China (D:Geoscience), 2007, 37(8):1081-1087.
[21] 马世欣,刘春桐,李洪才,等. 基于空谱联合聚类的改进核协同高光谱异常检测[J]. 光子学报,2019, 48(1):0110003. MA Shixin, LIU Chuntong, LI Hongcai, et al. Improved collaborative algorithm based on spatial-spectral joint clustering for hyperspectral anomaly detection[J]. Acta Photonica Sinica, 2019, 48(1):0110003.
[22] EISMANN M T, SCHWARTZ C R, CEDERQUIST J N, et al. Comparison of infrared imaging hyperspectral sensors for military target detection applications[C]//Proceedings of 1996 International Symposium on Optical Science, Engineering, and Instrumentation.Denver:[s.n.], 1996:91-101.
[23] 方红亮,田庆久. 高光谱遥感在植被监测中的研究综述[J]. 遥感技术与应用,1998, 13(1):62-69. FANG Hongliang, TIAN Qingjiu. A review of hyperspectral remote sensing in vegetation monitoring[J]. Remote Sensing Technology and Application, 1998, 13(1):62-69.
[24] 张杰林,曹代勇. 成像光谱数据挖掘与矿物填图技术研究[J]. 遥感技术与应用, 2002, 17(5):259-263. ZHANG Jielin, CAO Daiyong. Study on data mining and mineral mapping technology of the imaging spectrum data[J]. Remote Sensing Technology and Application, 2002, 17(5):259-263.
[25] 袁静,章毓晋,高方平. 线性高光谱解混模型综述[J]. 红外与毫米波学报, 2018, 37(5):553-571. YUAN Jing, ZHANG Yujin, GAO Fangping. An overview on linear hyperspectral unmixing[J]. Journal of Infrared and Millimeter Waves, 2018, 37(5):553-571.
[26] 杨斌,王斌. 高光谱遥感图像非线性解混研究综述[J]. 红外与毫米波学报,2017, 36(2):173-185. YANG Bin, WANG Bin. Review of nonlinear unmixing for hyperspectral remote sensing imagery[J]. Journal of Infrared and Millimeter Waves, 2017, 36(2):173-185.
[27] HAPKE B. Bidirectional reflectance spectroscopy:I-theory[J]. Journal of Geophysical Research, 1981, 86(B4):3039-3054.
[28] BORSOI R A, IMBIRIBA T, BERMUDEZ J C M, et al. Spectral variability in hyperspectral data unmixing:a comprehensive review[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(4):223-270.
[29] CHEN Xuehong, CHEN Jin, JIA Xiuping, et al. A quantitative analysis of virtual endmembers increased impact on the collinearity effect in spectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(8):2945-2956.
[30] HONG Danfeng, YOHOYA N, CHANUSSOT J, et al. An augmented linear mixing model to address spectral variability for hyperspectral unmixing[J]. IEEE Transactions on Image Processing, 2019, 28(4):1923-1938.
[31] DOBIGEON N, TOURNERET J Y, RICHARD C, et al. Nonlinear unmixing of hyperspectral images:models and algorithms[J]. IEEE Signal Processing Magazine, 2014, 31(1):82-94.
[32] 童庆禧,张兵,郑兰芬. 高光谱遥感-原理、技术与应用[M]. 北京:高等教育出版社,2006. TONG Qingxi, ZHANG Bing, ZHENG Lanfen. Hyperspectral remote sensing-principles, techniques and applications[M]. Beijing:Higher Education Press,2006.
[33] 蓝金辉, 邹金霖, 郝彦爽, 等. 高光谱遥感影像混合像元分解研究进展[J]. 遥感学报, 2018, 22(1):13-27. LAN Jinhui, ZOU Jinlin, HAO Yanshuang, et al. Research progress on unmixing of hyperspectral remote sensing imagery[J]. Journal of Remote Sensing, 2018, 22(1):13-27.
[34] BOARDMAN J, KRUSE F, GREEN R. Mapping target signatures via partial unmixing of AVIRIS data[C]//Proceedings of 1995 JPL Airborne Earth Science Workshop. Pasadena:JPL Publication, 1995:23-26.
[35] HARSANYI J C, CHANG C. Hyperspectral image classification and dimensionality reduction:an orthogonal subspace projection approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4):779-785.
[36] TURK M, PENTLAND A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3:71-86.
[37] CHAN Tsunghan, CHI Chongyung, HUANG Yumin, et al. A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing[J]. IEEE Transactions on Signal Processing, 2009, 57(11):4418-4432.
[38] WINTER M E. N-FINDR:an algorithm for fast autonomous spectral end-member determination in hyperspectral data[C]//Proceedings of 1999 Imaging Spectrometry V, International Society for Optics and Photonics. Denver:[s.n.], 1999:266-275.
[39] NASCIMENTO J M P, BIOUCAS J M. Vertex component analysis:a fast algorithm to unmix hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4):898-910.
[40] ARNGREN M, SCHMIDT M N, LARSEN J. Unmixing of hyperspectral images using Bayesian non-negative matrix factorization with volume prior[J]. Journal of Signal Processing Systems, 2011, 65(3):479-496.
[41] TSINOS C G, RONTOGIANNIS A A, BERBERIDIS K. Distributed blind hyperspectral unmixing via joint sparsity and low-rank constrained non-negative matrix factorization[J]. IEEE Transactions on Computation Imaging, 2017, 3(2):160-174.
[42] HE Wei, ZHANG Hongyan, ZHANG Liangpei. Total variation regularization reweighted sparse nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7):3909-3921.
[43] SHI Zhenwei, SHI Tianyang, ZHOU Min, et al. Collaborative sparse hyperspectral unmixing using L₀ norm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9):5495-5508.
[44] SONG Conghe. Spectral mixture analysis for subpixel vegetation fractions in the urban environment:how to incorporate endmember variability?[J] Remote Sensing of Environment, 2005, 95(2):248-263.
[45] KESHAVA N, MUSTARD J F. Spectral unmixing[J]. IEEE Signal Processing Magazine, 2002, 19(1):44-57.
[46] 李二森, 朱述龙, 周晓明, 等. 高光谱图像端元提取算法研究进展与比较[J]. 遥感学报, 2011, 15(4):659-679. LI Ersen, ZHU Shulong, ZHOU Xiaoming, et al. The development and comparison of endmember extraction algorithms using hyperspectral imagery[J]. Journal of Remote Sensing, 2011, 15(4):659-679.
[47] IORDACHE MD, BIOUCAS-DIAS J M, PLAZA A. Sparse unmixing of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6):2014-2039.
[48] IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A. Collaborative sparse regression for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1):341-354.
[49] IORDACHE M D, BIOUCAS-DIAS J M, PLAZA A, et al. MUSIC-CSR:hyperspectral unmixing via multiple signal classification and collaborative sparse regression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7):4364-4382.
[50] ROGGE D M, RIVARD B, ZHANG Jinkai, et al. Iterative spectral unmixing for optimizing per-pixel endmember sets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(12):3725-3736.
[51] DOBIGEON N, MOUSSAOUI S, COULON M, et al. Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery[J].IEEE Transactions on Signal Processing, 2009, 57(11):4355-4368.
[52] DENNISON P E, ROBERTS D A. Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE[J]. Remote Sensing of Environment, 2003, 87(2-3):123-135.
[53] BIOUCAS D J M, FIGUEIREDO M A T. Alternating direction algorithms for constrained sparse regression:application to hyperspectral unmixing[C]//Proceedings of 2010 IEEE Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing. Reykjavik:IEEE,2010:1-4.
[54] ECKSTEIN J, BERTSSEKAS D P. On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators[J]. Mathematical Programming, 1992, 55(1):293-318.
[55] SIGURDSSON J, ULFARSON M O, SVEINSSON J R. Blind hyperspectral unmixing using total variation and L_q sparse regularization[J]. IEEE Transactions on Geoscience and Remote Sensing,2016, 54(11):6371-6384.
[56] XU Xia, SHI Zhenwei, PAN Bin. L₀-based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 141:46-59.
[57] IORDACHE M D, BIOUCAS D J M, PLAZA A. Total variation spatial regularization for sparse hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11):4484-4502.
[58] ZHONG Yanfei, FENG Ruyi,ZHANG Liangpei. Non-local sparse unmixing for hyperspectral remote sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):1889-1909.
[59] ZHANG Shaoquan, LI Jun, LI Hengchao, et al. Spectral-spatial weighted sparse regression for hyperspectral image unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6):3265-3276.
[60] BORSOI R A, IMBIRIBA T, BERMUDEZ J C M, et al. A fast multiscale spatial regularization for sparse hyperspectral unmixing[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(4):598-602.
[61] LI Hao, FENG Ruyi, WANG Lizhe, et al. Superpixel-based reweighted low-rank and total variation sparse unmixing for hyperspectral remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1):629-647.
[62] CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1):129-159.
[63] 余先川,李建广,徐金东,等. 基于二次散射的高光谱遥感图像光谱非线性混合模型[J]. 国土资源遥感,2013, 25(1):18-25. YU Xianchuan, LI Jianguang, XU Jindong, et al. A nonlinear spectral mixture model for hyperspectral imagery based on secondary scattering[J]. Remote Sensing for Land & Resources, 2013, 25(1):18-25.
[64] HEYLEN R, SCHEUNDERS P. A multilinear mixing model for nonlinear spectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1):240-251.
[65] ZHANG Liangpei, WU Bo, HUANG Bo, et al. Nonlinear estimation of subpixel proportion via kernel least square regression[J]. International Journal of Remote Sensing, 2007, 28(18):4157-4172.
[66] CHI J, CRAWFORD M M. Selection of landmark points on nonlinear manifolds for spectral unmixing using local homogeneity[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4):711-715.
[67] SU Yuanchao, LI Jun, PLAZA Antonio, et al. DAEN:deep autoencoder networks for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7):4309-4321.
[68] 刘帅, 邢光龙. 高光谱图像混合像元多维卷积网络协同分解法[J]. 测绘学报, 2020, 49(12):1600-1608. DOI:10.11947/j.AGCS.2020.20190461. LIU Shuai, XING Guanglong. Multi-dimensional convolutional network collaborative unmixing method for hyperspectral image mixed pixels[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(12):1600-1608. DOI:10.11947/j.AGCS.2020.20190461.
[69] HEYLEN R, SCHEUNDERS P. A multilinear mixing model for nonlinear spectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1):240-251.
[70] GUILFOYLE K J, ALTHOUSE M L, CHANG CI. A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(8):2314-2318.
[71] PLAZA J, PLAZA A, PEREZ R, et al. On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images[J]. Pattern Recognition, 2009, 42(11):3032-3045.
[72] ZHANG Xiangrong, SUN Yujia, ZHANG Jingyan, et al. Hyperspectral unmixing via deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(11):1755-1759.
[73] WANG Mou, ZHAO Min, CHEN Jie, et al. Nonlinear unmixing of hyperspectral data via deep autoencoder networks[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9):1467-1471.
[74] 韩竹,高连如,张兵,等. 高分五号高光谱图像自编码网络非线性解混[J]. 遥感学报,2020, 24(4):399-400. HAN Zhu, GAO Lianru, ZHANG Bing, et al. Nonlinear hyperspectral unmixing algorithm based on deep autoencoder networks[J]. Journal of Remote Sensing, 2020, 24(4):399-400.
[75] LI Zeng, CHEN Jie, RAHARDJA S. Kernel-based nonlinear spectral unmixing with dictionary pruning[J]. Remote Sensing, 2019, 11(5):529.
[76] HEYLEN R, BURAZEROVIC D, SCHEUNDERS P. Non-linear spectral unmixing by geodesic simplex volume maximization[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3):534-542.
[77] 吴波, 张良培, 李平湘. 基于支撑向量回归的高光谱混合像元非线性分解[J]. 遥感学报, 2006, 10(3):312-319. WU Bo, ZHANG Liangpei, LI Pingxiang. Unmixing hyperspectral imagery based on support vector nonlinear approximating regression[J]. Journal of Remote Sensing, 2006, 10(3):312-319.
[78] PALSSON B, ULFARSSON M O, SVEINSSON J R. Convolutional autoencoder for spectral spatial hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1):535-549.
[79] YAO Jing, HONG Danfeng, XU Lin, et al. Sparsity-enhanced convolutional decomposition:a novel tensor-based paradigm for blind hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:5505014.
[80] NGUYEN Q M, ATKINSON P M, LEWIS H G. Super-resolution mapping using hopfield neural network with panchromatic imagery[J]. International Journal of Remote Sensing, 2011, 32(21):6149-6176.
[81] ZHONG Yanfei, ZHANG Liangpei. Remote sensing image sub-pixel mapping based on adaptive differential evolution[J]. IEEE Transactions on Systems, Man, and Cybernetics, 2012, 42(5):1306-1329.
[82] JIN Huiran, MOUNTRAKIS G, LI Peijun. A super-resolution mapping method using local indicator variograms[J]. International Journal of Remote Sensing, 2012, 33(24):7747-7773.
[83] TOLPEKIN V A, STEIN A. Quantification of the effects of land-cover-class spectral separability on the accuracy of Markov-random-field-based superresolution mapping[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(9):3283-3297.
[84] TONG Xiaohua, ZHANG Xue, SHAN Jie, et al. Attraction-repulsion model-based subpixel mapping of multi-hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5):2799-2814.
[85] SU Yuanfong, FOODY G, MUAD A M, et al. Combining hopfield neural network and contouring methods to enhance super-resolution mapping[J].IEEE Journal of Selection Topics in Applied Earth Observation and Remote Sensing, 2012, 5(5):1403-1417.
[86] 宋蜜. 基于多目标优化理论的高光谱遥感影像亚像元制图方法研究[D]. 武汉:武汉大学, 2021. SONG Mi. Sparsity multiobjective optimization based subpixel mapping for hyperspectral remote sensing imagery[D]. Wuhan:Wuhan University, 2021.
[87] MERTENS K C, DE BAETS B, VERBEKE L P C, et al. A sub-pixel mapping algorithm based on sub-pixel/pixel spatial attraction models[J]. International Journal of Remote Sensing, 2006, 27(15):3293-3310.
[88] SHEN Zhangquan, QI Jiaguo, WANG Ke. Modification of pixel-swapping algorithm with initialization from a sub-pixel/pixel spatial attraction model[J]. Photogrammetric Engineering and Remote Sensing, 2009, 75(5):557-567.
[89] GE Yong, CHEN Yuehong, STEIN A, et al. Enhanced subpixel mapping with spatial distribution patterns of geographical objects[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4):2356-2370.
[90] ZHONG Yanfei, WU Yunyun, XU Xiong, et al. An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1411-1426.
[91] FENG Ruyi, ZHONG Yanfei, XU Xiong, et al. Adaptive sparse subpixel mapping with a total variation model for remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5):2855-2872.
[92] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521:436-444.
[93] 何达. 遥感影像全局时空谱融合亚像元制图方法研究[D]. 武汉:武汉大学, 2020. HE Da. Spectral-spatial-temporal global fusion in sub-pixel mapping for hyperspectral remote sensing imagery[D]. Wuhan:Wuhan University, 2020.
[94] ARUN P V, BUDDHIRAJU K M, PORWAL A. CNN based sub-pixel mapping for hyperspectral images[J]. Neurocomputing, 2018, 311:51-64.
[95] LING F, FOODY G M. Super-resolution land cover mapping by deep learning[J]. Remote Sensing Letters, 2019, 10(6):598-606.
[96] HE Da, SHI Qian, LIU Xiaoping, et al. Spectral-spatial fusion sub-pixel mapping based on deep neural network[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:6004105.
[97] HE Da, ZHONG Yanfei, ZHANG Liangpei. Spatiotemporal subpixel geographical evolution mapping[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(4):2198-2220.
[98] XU Xiong, ZHONG Yanfei, ZHANG Liangpei, et al. Sub-pixel mapping based on a MAP model with multiple shifted hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):580-593.
[99] WANG Qunming, SHI Wenzhong, ATKINSON P M. Spatiotemporal subpixel mapping of time-series images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6):5397-5411.
[100] LING Feng, LI Xiaodong, DU Yun, et al. Super-resolution land cover mapping with spatial-temporal dependence by integrating a former fine resolution map[J]. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2014,7(5):1816-1825.
[101] HE Da, ZHONG Yanfei, FENG Ruyi, et al. Spatial-temporal sub-pixel mapping based on swarm intelligence theory[J]. Remote Sensing, 2016, 8(11):894.
[102] HE Da, ZHONG Yanfei, ZHANG Liangpei. Spectral-spatial-temporal MAP-based sub-pixel mapping for land-cover change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(3):1696-1717.
[103] WANG Qunming, SHI Wenzhong. Utilizing multiple subpixel shifted images in subpixel mapping with image interpolation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(4):798-802.
[104] LING Feng, DU Yun, XIAO Fei, et al. Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images[J]. International Journal of Remote Sensing, 2010, 31(19):5023-5040.
[105] WANG Peng, WANG Liguo, MURA M D, et al. Using multiple subpixel shifted images with spatial-spectral information in soft-then-hard subpixel mapping[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017,10(6):2950-2959.
[106] ZHONG Yanfei, WU Yunyun, ZHANG Liangpei, et al. Adaptive MAP subpixel mapping model based on regularization curve for multiple shifted hyperspectral imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 96:134-148.
[107] 杜博. 高光谱遥感影像亚像元小目标探测研究[D]. 武汉:武汉大学, 2010. DU Bo. Sub-pixel target detection from hyperspectral remote sensing imagery[D]. Wuhan:Wuhan University, 2010.
[108] 凌强. 高光谱遥感图像小目标检测与识别技术研究[D]. 长沙:国防科技大学, 2019. LING Qiang. Small target detection and recognition for hyperspectral remote sensing imagery[D]. Changsha:National University of Defense Technology, 2019.
[109] 单兰鑫. 高光谱图像亚像元小目标检测[D]. 西安:西安电子科技大学, 2017. SHAN Lanxin. Sub-pixel target identified in hyperspectral remote sensing imagery[D]. Xi'an:Xidian University, 2017.
[110] REED I S, YU Xiaoli. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990, 38(10):1760-1770.
[111] MOLERO J M, GARZON E M, GARCIA I, et al. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2013,6(2):801-814.
[112] SCHAUM A. Joint subspace detection of hyperspectral targets[C]//Proceedings of 2004 IEEE Aerospace Conference. Big Sky:IEEE, 2004, 3:1818-1824.
[113] NASRABADI N M. Regularization for spectral matched filter and RX anomaly detector[J]. International Society for Optics and Photonics, 2008, 6966:696604.
[114] KWON H, NASRABADI N M. Kernel RX-algorithm:a nonlinear anomaly detector for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2):388-397.
[115] KELLY E J. An adaptive detection algorithm[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22(1):115-127.
[116] KRAUT S, SCHARF L L. The CFAR adaptive subspace detector is a scale-invariant GLRT[J]. IEEE Transactions on Signal Processing, 1999, 47(9):2538-2541.
[117] SCHARF L L, FRIEDLANDER B. Matched subspace detectors[J]. IEEE Transactions on Signal Processing, 1994, 42(8):2146-2157.
[118] ZHANG Yuan, HE Mingyi, MEI Shaohui. Target detection of multi-spectral image based on PCA and ICA[J]. Remote Sensing Technology and Application, 2006, 21(3):227-231.
[119] FARRELL M D, MERSEREAU R M. On the impact of PCA dimension reduction for hyperspectral detection of difficult targets[J]. IEEE Geoscience and Remote Sensing Letters, 2005, 2(2):192-195.
[120] YUAN Zongze, SUN Hao, JI Kefeng, et al. Local sparsity divergence for hyperspectral anomaly detection[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10):1697-1701.
[121] LI Jiayi, ZHANG Hongyan, ZHANG Liangpei, et al. Hyperspectral anomaly detection by the use of background joint sparse representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6):2523-2533.
[122] ZHANG Yuxiang, DU Bo, ZHANG Liangpei, et al. Joint sparse representation and multitask learning for hyperspectral target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2):894-906.
[123] LI Wei, DU Qian. Collaborative representation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1463-1474.
[124] CANDES E J, LI Xiaodong, MA Yi, et al. Robust principal component analysis?[J] Journal of the ACM, 2011, 58(3):1-37.
[125] LI Shuangjiang, WANG Wei, QI Hairong, et al. Low-rank tensor decomposition based anomaly detection for hyperspectral imagery[C]//Proceedings of 2015 IEEE International Conference on Image Processing. New York:IEEE, 2015:4525-4529.
[126] ZHANG Yuxiang, DU Bo, ZHANG Liangpei, et al. A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3):1376-1389.
[127] CHEN Jinhui, YANG Jian. Robust subspace segmentation via low-rank representation[J]. IEEE Transactions on Cybernetics, 2014, 44(8):1432-1445.
[128] XU Yang, WU Zebin, LI Jun, et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4):1990-2000.
[129] QU Ying, WANG Wei, GUO Rui, et al. Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8):4391-4405.
[130] CHENG Tongkai, WANG Bin. Graph and total variation regularized low-rank representation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1):391-406.
[131] FENG Ruyi, LI Hao, WANG Lizhe, et al. Local spatial constraint and total variation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:5512216.
[132] HEINZ D C, CHANG C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3):529-545.
[133] CHANG C I, HEINZ D C. Constrained subpixel target detection for remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(3):1144-1159.
[134] 张兵,陈正超,郑兰芬,等. 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[J]. 红外与毫米波学报, 2004, 23(6):441-445. ZHANG Bing, CHEN Zhengchao, ZHENG Lanfen, et al. Object detection based on feature extraction from hyperspectral imagery and convex cone projection transform[J]. Journal of Infrared and Millimeter Waves, 2004, 23(6):441-445.
[135] 沈银河. 高光谱图像亚像元级目标检测的非线性方法研究[D]. 杭州:杭州电子科技大学, 2011. SHEN Yinhe. Research on nonlinear methods for subpixel target detection in hyperspectral imagery[D]. Hangzhou:Hangzhou Dianzi University, 2011.
[136] RASTI B, HONG Danfeng, HANG Renlong, et al. Feature extraction for hyperspectral imagery, the evolution from shallow to deep:overview and toolbox[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 8(4):60-88.
[137] LIU Da, LI Jianxun. Spectral-spatial target detection based on data field modeling for hyperspectral data[J]. Chinese Journal of Aeronautics, 2018, 31(4):795-805.
[138] LIU Yongjian, GAO Guoming, GU Yanfeng. Tensor matched subspace detector for hyperspectral target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4):1967-1974.
[139] NASRABADI N M. Hyperspectral target detection:an overview of current and future challenges[J]. IEEE Signal Processing Magazine, 2014, 31(1):34-44.
[140] ZGONG Ping, GONG Zhiqiang, SHAN Jiaxin. Multiple instance learning for multiple diverse hyperspectral target characterizations[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(1):246-258.
[141] SHI Yanzi, LEI Jie, YIN Yaping, et al. Discriminative feature learning with distance constrained stacked sparse autoencoder for hyperspectral target detection[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(9):1462-1466.