[1] 张兵. 高光谱图像处理与信息提取前沿[J]. 遥感学报, 2016, 20(5):1062-1090. ZHANG Bing. Frontiers in hyperspectral image processing and information extraction[J] Journal of Remote Sensing, 2016, 20(5):1062-1090. [2] 樊星皓, 刘春雨, 金光, 等. 轻小型高分辨率星载高光谱成像光谱仪[J]. 光学精密工程, 2021, 29(3):463-473. FAN Xinghao, LIU Chunyu, JIN Guang, et al. Light and small high-resolution spaceborne hyperspectral imaging spectrometer[J]. Optics and Precision Engineering, 2021, 29(3):463-473. [3] ZHONG Yanfei, WANG Xinyu, WANG Shaoyu, et al. Advances in spaceborne hyperspectral remote sensing in China[J]. Geo-spatial Information Science, 2021, 24(1):95-120. [4] ZHONG Yanfei, WANG Xinyu, XU Yao, et al. Mini-UAV-borne hyperspectral remote sensing:from observation and processing to applications[J]. IEEE Geoscience and Remote Sensing Magazine, 2018, 6(4):46-62. [5] 邵晓鹏, 刘飞, 李伟, 等. 计算成像技术及应用最新进展[J]. 激光与光电子学进展, 2020, 57(2):11-55. SHAO Xiaopeng, LIU Fei, LI Wei, et al. Latest progress in computational imaging technology and application[J]. Laser & Optoelectronics Progress, 2020, 57(2):11-55. [6] TAN Kun, WU Fuyu, DU Qian, et al. A parallel Gaussian-Bernoulli restricted Boltzmann machine for mining area classification with hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 2019, 12(2):627-636. [7] RANGNEKAR A, MOKASHI N, IENTILUCCI E J, et al. Aerorit:a new scene for hyperspectral image analysis[J]. IEEE Transactions on Geoscience Remote Sensing, 2020, 58(11):8116-8124. [8] 岑奕, 张立福, 张霞, 等. 雄安新区马蹄湾村航空高光谱遥感影像分类数据集[J]. 遥感学报, 2020, 24(11):1299-1306. CEN Yi, ZHANG Lifu, ZHANG Xia, et al. Aerial hyperspectral remote sensing classification dataset of Xiongan New Area (Matiwan Village)[J] Journal of Remote Sensing, 2020, 24(11):1299-1306 [9] MÒTTUS M, PHAM P, HALME E, et al. TAIGA:a novel dataset for multitask learning of continuous and categorical forest variables from hyperspectral imagery[J]. Transactions on Geoscience Remote Sensing, 2022, 60:1-11. [10] XU Yue, GONG Jianya, HUANG Xin, et al. Luojia-HSSR:a high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet[J]. Geo-spatial Information Science, 2022:1-13. [11] ZHONG Yanfei, HU Xin, LUO Chang, et al. WHU-Hi:UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF[J]. Remote Sensing of Environment, 2020, 250:112012. [12] WANG Shaoyu, WANG Xinyu, ZHONG Yanfei, et al. Hyperspectral anomaly detection via locally enhanced low-rank prior[J]. Transactions on Geoscience Remote Sensing, 2020, 58(10):6995-7009. [13] NIU Bowen, FENG Quanlong, CHEN Boan, et al. HSI-TransUNet:a transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery[J]. Computers Electronics in Agriculture, 2022, 201:107297. [14] 张雪红. 基于高分辨率遥感的桉树林空间异质性与尺度效应研究[D]. 南京:南京大学, 2012. ZHANG Xuehong. Study on spatial heterogeneity and scale effect of eucalyptus forest based on high resolution remote sensing[D]. Nanjing:Nanjing University, 2012. [15] SUN Weiwei, DU Qian. Hyperspectral band selection:a review[J]. IEEE Geoscience Remote Sensing Magazine, 2019, 7(2):118-139. [16] CHANG CI, DU Qian, SUN TL, et al. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification[J]. IEEE Transactions on Geoscience Remote Sensing, 1999, 37(6):2631-2641. [17] GUO Baofeng, GUNN S R, DAMPER R I, et al. Band selection for hyperspectral image classification using mutual information[J]. IEEE Geoscience Remote Sensing Letters, 2006, 3(4):522-526. [18] GU Shenkai, CHENG Ran, JIN Yaochu. Feature selection for high-dimensional classification using a competitive swarm optimizer[J]. Soft Computing, 2018, 22(3):811-822. [19] PUDIL P, NOVOVIČOVÁ J, KITTLER J. Floating search methods in feature selection[J]. Pattern Recognition Letters, 1994, 15(11):1119-1125. [20] 杜卓明, 冯静. 改进遗传算法和支持向量机的特征选择算法[J]. 计算机工程与应用, 2009, 45(29):28-30. DU Zhuoming, FENG Jing. Support vector machine feature selection algorithm based on modified genetic algorithm[J]. Computer Engineering and Applications, 2009, 45(29):28-30. [21] WANG Qi, LI Qiang, LI Xuelong. A fast neighborhood grouping method for hyperspectral band selection[J]. IEEE Transactions on Geoscience Remote Sensing, 2020, 59(6):5028-5039. [22] ZHU Guokang, HUANG Yuancheng, LEI Jingsheng, et al. Unsupervised hyperspectral band selection by dominant set extraction[J]. IEEE Transactions on Geoscience Remote Sensing, 2015, 54(1):227-239. [23] LI Shuangjiang, QI Hairong. Sparse representation based band selection for hyperspectral images[C]//Proceedings of 2011 IEEE International Conference on Image Processing. Brussels:IEEE, 2011:2693-2696. [24] SUN Weiwei, ZHANG Liangpei, DU Bo, et al. Band selection using improved sparse subspace clustering for hyperspectral imagery classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations Remote Sensing, 2015, 8(6):2784-2797. [25] ARCHIBALD R, FANN G. Feature selection and classification of hyperspectral images with support vector machines[J]. IEEE Geoscience Remote Sensing Letters, 2007, 4(4):674-677. [26] HE Ke, SUN Weiwei, YANG Gang, et al. A dual global-local attention network for hyperspectral band selection[J]. IEEE Transactions on Geoscience Remote Sensing, 2022, 60:1-13. [27] CAI Yaoming, LIU Xiaobo, CAI Zhihua. BS-Nets:an end-to-end framework for band selection of hyperspectral image[J]. IEEE Transactions on Geoscience Remote Sensing, 2019, 58(3):1969-1984. [28] LIU Jia, XIANG Jianjian, JIN Yongjun, et al. Boost precision agriculture with unmanned aerial vehicle remote sensing and edge intelligence:a survey[J]. Remote Sensing, 2021, 13(21):4387. [29] CHEN Huayue, MIAO Fang, CHEN Yijia, et al. A hyperspectral image classification method using multifeature vectors and optimized KELM[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:2781-2795. [30] XUE Zhaohui, NIE Xiangyu. Low-rank and sparse representation with adaptive neighborhood regularization for hyperspectral image classification[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(1):73. [31] YU Wentao, WAN Sheng, LI Guangyu, et al. Hyperspectral image classification with contrastive graph convolutional network[J]. IEEE Transactions on Geoscience Remote Sensing, 2023, 61:1-15. [32] ZHANG Shichao, ZHANG Jiahua, XUN Lan, et al. AMFAN:adaptive multiscale feature attention network for hyperspectral image classification[J]. IEEE Geoscience Remote Sensing Letters, 2022, 19:1-5. [33] OUYANG E, LI B, HU W, et al. When multigranularity meets spatial-spectral attention:a hybrid transformer for hyperspectral image classification[J]. IEEE Transactions on Geoscience Remote Sensing, 2023, 61:1-18. [34] GE Haimiao, WANG Liguo, LIU Moqi, et al. Two-branch convolutional neural network with polarized full attention for hyperspectral image classification[J]. Remote Sensing, 2023, 15(3):848. [35] WANG Kexian, ZHENG Shunyi, LI Rui, et al. A deep double-channel dense network for hyperspectral image classification[J]. Journal of Geodesy & Geoinformation Science, 2021, 4(4). [36] HANG Renlong, LI Zhu, LIU Qingshan, et al. Hyperspectral image classification with attention-aided CNNs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(3):2281-2293. [37] ZHENG Zhuo, ZHONG Yanfei, MA Ailong, et al. FPGA:fast patch-free global learning framework for fully end-to-end hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58:5612-5626. [38] HU Xin, ZHONG Yanfei, WANG Xinyu, et al. SPNet:spectral patching end-to-end classification network for UAV-borne hyperspectral imagery with high spatial and spectral resolutions[J]. IEEE Transactions on Geoscience Remote Sensing, 2022, 60:1-17. [39] HU Xin, WANG Xinyu, ZHONG Yanfei, et al. S3ANet:spectral-spatial-scale attention network for end-to-end precise crop classification based on UAV-borne H2 imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 183:147-163. [40] DONG Yanni, LIU Quanwei, DU Bo, et al. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 2022, 31:1559-1572. [41] CHEN Yushi, JIANG Hanlu, LI Chunyang, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):6232-6251. [42] MAKANTASIS K, KARANTZALOS K, DOULAMIS A, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milano:IEEE, 2015:4959-4962. [43] AHMAD M, KHAN A M, MAZZARA M, et al. A fast and compact 3D CNN for hyperspectral image classification[J]. IEEE Geoscience Remote Sensing Letters, 2020, 19:1-5. [44] ROY S K, KRISHNA G, DUBEY S R, et al. HybridSN:exploring 3D-2D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(2):277-281. [45] MEI Xiaoguang, PAN Erting, MA Yong, et al. Spectral-spatial attention networks for hyperspectral image classification[J]. Remote Sensing, 2019, 11(8):963. [46] WANG Di, DU Bo, ZHANG Liangpei. Fully contextual network for hyperspectral scene parsing[J]. IEEE Transactions on Geoscience Remote Sensing, 2021, 60:1-16. [47] ZHU Qiqi, DENG Weihuan, ZHENG Zhuo, et al. A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification[J]. IEEE Transactions on Cybernetics, 2021, 52(11):11709-11723. [48] CHANG C I. Hyperspectral target detection:hypothesis testing signal-to-noise ratio and spectral angle theories[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60:1-23. [49] 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. [50] 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):1377-1394. ZHANG Liangpei. Advance and future challenges in hyperspectral target detection[J]. Geomatics and Information Science of Wuhan University, 2014, 39(12):1377-1394. [51] SCHARF L L, FRIEDLANDER B. Matched subspace detectors[J]. IEEE Transactions on Signal Processing, 1993, 42(8):2146-2157. [52] HARSANYI J C. Detection and classification of subpixel spectral signatures in hyperspectral image sequences[M]. Baltimore:ProQuest Dissertations Publishing, 1993. [53] MANOLAKIS D, SHAW G. Detection algorithms for hyperspectral imaging applications[J]. Signal Processing Magazine IEEE, 2002, 19(1):29-43. [54] KWON H, NASRABADI N M. Kernel matched subspace detectors for hyperspectral target detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 28(2):178-194. [55] CAPOBIANCO L, GARZELLI A, CAMPS-VALLS G. Target detection with semisupervised kernel orthogonal subspace projection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(11):3822-3833. [56] CHEN Yi, NASRABADI N M, TRAN T D. Sparse representation for target detection in hyperspectral imagery[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3):629-640. [57] LI Wei, DU Qian, ZHANG Bing. Combined sparse and collaborative representation for hyperspectral target detection[J]. Pattern Recognition, 2015, 48(12):3904-3916. [58] 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, 2016, 55(2):894-906. [59] 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. [60] SHI Yanzi, LI Jiaojiao, ZHENG Yuxuan, et al. Hyper-spectral target detection with RoI feature transformation and multiscale spectral attention[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(6):5071-5084. [61] RAO Weiqiang, GAO Lianru, QU Ying, et al. Siamese transformer network for hyperspectral image target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-19. [62] ZHANG Yiming, DU Bo, ZHANG Yuxiang, et al. Spatially adaptive sparse representation for target detection in hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11):1923-1927. [63] WANG Shaoyu, ZHONG Yanfei, ZHAO Ji, et al. S3 CRF:sparse spatial-spectral conditional random field target detection framework for airborne hyperspectral data[J]. IEEE Access, 2020, 8:46917-46930. [64] STEIN D W J, BEAVEN S G, HOFF L E, et al. Anomaly detection from hyperspectral imagery[J]. IEEE Signal Processing Magazine, 2002, 19(1):58-69. [65] 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. [66] 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. [67] BANERJEE A, BURLINA P, DIEHL C. A support vector method for anomaly detection in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8):2282-2291. [68] WEI Li, QIAN Du. Collaborative representation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1463-1474. [69] 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. [70] LI Lu, LI Wei, DU Qian, et al. Low-rank and sparse decomposition with mixture of Gaussian for hyperspectral anomaly detection[J]. IEEE Transactions on Cybernetics, 2020, 51(9):4363-4372. [71] 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, 2015, 54(4):1990-2000. [72] 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, 2015, 54(3):1376-1389. [73] FAN Ganghui, MA Yong, MEI Xiaoguang, et al. Hyperspectral anomaly detection with robust graph autoencoders[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60:1-14. [74] XIE Weiying, LEI Jie, LIU Baozhu, et al. Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detection[J]. Neural Networks, 2019, 119:222-234. [75] LU Xiaoqiang, ZHANG Wuxia, HUANG Ju. Exploiting embedding manifold of autoencoders for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(3):1527-1537. [76] YING Qu, WEI Wang, RUI Guo, 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. [77] WANG Shaoyu, WANG Xinyu, ZHANG Liangpei, et al. Auto-AD:autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder[J]. IEEE Transactions on Geoscience Remote Sensing, 2021, 60:1-14. [78] ZARCO-TEJADA P J, GUILLÉN-CLIMENT M L, HERNÁNDEZ-CLEMENTE R, et al. Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)[J]. Agricultural Forest Meteorology, 2013, 171:281-294. [79] ZARCO-TEJADA P J, MORALES A, TESTI L, et al. Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance[J]. Remote Sensing of Environment, 2013, 133:102-115. [80] AASEN H, BURKART A, BOLTEN A, et al. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring:from camera calibration to quality assurance[J]. ISPRS Journal of Photogrammetry Remote Sensing, 2015, 108:245-259. [81] SANKEY T T, MCVAY J, SWETNAM T L, et al. UAV hyperspectral and LiDAR data and their fusion for arid and semi-arid land vegetation monitoring[J]. Remote Sensing in Ecology Conservation, 2018, 4(1):20-33. [82] ZHAO Hengwei, ZHONG Yanfei, WANG Xinyu, et al. Mapping the distribution of invasive tree species using deep one-class classification in the tropical montane landscape of Kenya[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 187:328-344. [83] LI Jingtao, WANG Xinyu, ZHAO Hengwei, et al. Detecting pine wilt disease at the pixel level from high spatial and spectral resolution UAV-borne imagery in complex forest landscapes using deep one-class classification[J]. International Journal of Applied Earth Observation Geoinformation, 2022, 112:102947. [84] JACKISCH R, LORENZ S, ZIMMERMANN R, et al. Drone-borne hyperspectral monitoring of acid mine drainage:an example from the Sokolov lignite district[J]. Remote Sensing, 2018, 10(3):385. [85] BOOYSEN R, JACKISCH R, LORENZ S, et al. Detection of REEs with lightweight UAV-based hyperspectral imaging[J]. Scientific Reports, 2020, 10(1):1-12. [86] LORENZ S, GHAMISI P, KIRSCH M, et al. Feature extraction for hyperspectral mineral domain mapping:a test of conventional and innovative methods[J]. Remote Sensing of Environment, 2021, 252:112129. [87] WEI Lifei, ZHANG Yangxi, LU Qikai, et al. Estimating the spatial distribution of soil total arsenic in the suspected contaminated area using UAV-borne hyperspectral imagery and deep learning[J]. Ecological Indicators, 2021, 133:108384. [88] 丁潇蕾. 基于水体吸收系数的城市黑臭水体遥感识别与分级方法研究[D]. 南京:南京师范大学, 2019. DING Xiaolei. Study on remote sensing identification and classification of urban black and odor water based on water absorption coefficient[D]. Nanjing:Nanjing Normal University, 2019. [89] WEI Lifei, HUANG Can, ZHONG Yanfei, et al. Inland waters suspended solids concentration retrieval based on PSO-LSSVM for UAV-borne hyperspectral remote sensing imagery[J]. Remote Sensing, 2019, 11(12):1455. [90] KWON Y S, PYO J C, KWON Y H, et al. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir[J]. Remote Sensing of Environment, 2020, 236:111517. [91] SU T C. A study of a matching pixel by pixel (MPP) algorithm to establish an empirical model of water quality mapping as based on unmanned aerial vehicle (UAV) images[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 58:213-224. [92] BECKER R H, SAYERS M, DEHM D, et al. Unmanned aerial system based spectroradiometer for monitoring harmful algal blooms:a new paradigm in water quality monitoring[J]. Journal of Great Lakes Research, 2019, 45(3):444-453. [93] KANG Xudong, WANG Zihao, DUAN Puhong, et al. The potential of hyperspectral image classification for oil spill mapping[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-15. |