Acta Geodaetica et Cartographica Sinica ›› 2023, Vol. 52 ›› Issue (7): 1187-1201.doi: 10.11947/j.AGCS.2023.20220491
• Special Issue of Hyperspectral Remote Sensing Technology • Previous Articles Next Articles
FENG Ruyi1,2, WANG Lizhe1, ZENG Tieyong2
Received:
2022-08-10
Revised:
2023-06-20
Published:
2023-07-31
Supported by:
CLC Number:
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.
[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 Lq 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. |
[1] | LI Shutao, WU Qiong, KANG Xudong. Hyperspectral remote sensing image intrinsic information decomposition: advances and challenges [J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1059-1073. |
[2] | DU Peijun, ZHANG Wei, ZHANG Peng, LIN Cong, GUO Shanchuan, HU Zezhou. A capsule network for hyperspectral image classification employing spatial-spectral feature [J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1090-1104. |
[3] | WEI Lifei, YU Ming, ZHONG Yanfei, YUAN Ziran, HUANG Can. Hyperspectral image classification method based on space-spectral fusion conditional random field [J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(3): 343-354. |
[4] | 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. |
[5] | HUANG Hong, SHI Guangyao, DUAN Yule, ZHANG Limei. Dimensionality reduction method for hyperspectral images based on weighted spatial-spectral combined preserving embedding [J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(8): 1014-1024. |
[6] | LIU Zhaoxin, ZHAO Liaoying, LI Xiaorun, CHEN Shuhan. Linear feature detection for hyperspectral subpixel mapping [J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1464-1474. |
[7] | LIU Boyu, CHEN Jun, XING Huaqiao, WU Hao, ZHANG Jun. A Spiral-based Construction of Adjacent Pixel Sets for Linear Spectral Unmixing [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(11): 1841-1849. |
[8] | WANG Junshu, JIANG Nan, ZHANG Guoming, LI Yang, LV Heng. Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information [J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9): 1003-1013. |
[9] | ZHANG Tao, LIU Jun, YANG Keming, LUO Wenshan, ZHANG Yuyu. Fusion Algorithm for Hyperspectral Remote Sensing Image Combined with Harmonic Analysis and Gram-Schmidt Transform [J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9): 1042-1047. |
[10] | TIAN Yugang, YANG Gui. A Fast Endmember Extraction Algorithm Using Spectrum Gradient Features [J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(2): 214-219. |
[11] | . Semi-supervised collaborative classification for hyperspectral remote sensing image with combination of cluster feature and SVM [J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(8): 855-861. |
[12] | . Sparse Unmixing for Hyperspectral Image Based on Spatial Homogeneous Analysis [J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(6): 607-612. |
[13] | . AN IMAGE FUSION METHOD BASED ON SPECTRUM PROJECTION AND WAVELET TRANSFORMING WITH HYPERSPECTRAL AND MULTISPECTRAL IMAGES [J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(2): 158-163. |
[14] | SHAO Yuanjie WU Guoping MA Li. Graph based Semi-Supervised Learning with Class-Probability Distance for Hyperspectral Remote Sensing Image Classification [J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(11): 1182-1189. |
[15] | . UNMIXING OF HYPERSPECTRAL MIXTURE PIXELS BASED ON SPECTRAL MULTISCALE SEGEMETED FEATURES [J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 205-212. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||