[1] 余旭初,冯伍法,杨国鹏,等. 高光谱影像分析与应用[M]. 北京:科学出版社,2013. YU Xuchu, FENG Wufa, YANG Guopeng, et al. Hyperspectral image analysis and application[M]. Beijing:Science Press, 2013. [2] 张良培,杜博,张乐飞. 高光谱遥感图像处理[M]. 北京:科学出版社,2012. ZHANG Liangpei, DU Bo, ZHANG Lefei. Hyperspectral remote sensing image processing[M]. Beijing:Science Press, 2012. [3] 中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会. GB/T 33462-2016基础地理信息1:10 000地形要素数据规范[S]. 北京:中国标准出版社,2016. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. GB/T 33462-2016 Fundamental geographic information specifications for 1:10 000 topographic data[S]. Beijing:Standards Press of China, 2016. [4] 丁胜,袁修孝,陈黎.粒子群优化算法用于高光谱遥感影像分类的自动波段选择[J]. 测绘学报, 2010, 39(3):257-263. DING Sheng, YUAN Xiuxiao, CHEN Li. Automatic band selection of hyperspectral remote sensing image classification using particle swarm optimization[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(3):257-263. [5] PATRO R N, SUBUDHI S, BISWAL P K, et al. A review on unsupervised band selection techniques:land cover classification for hyperspectral earth observation data[J]. IEEE Geoscience and Remote Sensing Magazine, 2021, 9(3):72-111. [6] FENG Jie, LI Di, GU Jing, et al. Deep reinforcement learning for semisupervised hyperspectral band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-19. [7] LIU Bing, YU Anzhu, ZHANG Pengqiang, et al. Active deep densely connected convolutional network for hyperspectral image classification[J]. International Journal of Remote Sensing, 2021, 42(15):5915-5934. [8] 石茜, 杜博, 张良培. 一种基于局部判别正切空间排列的高光谱遥感影像降维方法[J]. 测绘学报, 2012, 41(3):417-420. SHI Qian, DU Bo, ZHANG Liangpei. A dimensionality reduction method for hyperspectral imagery based on local discriminative tangent space alignment[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(3):417-420. [9] MURA M D, BENEDIKTSSON J A, WASKE B, et al. Morphological attribute profiles for the analysis of very high resolution images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10):3747-3762. [10] LIU Bing, GUO Wenyue, CHEN Xin, et al. Morphological attribute profile cube and deep random forest for small sample classification of hyperspectral image[J]. IEEE Access, 2020, 56:117096-117108. [11] LI Wei, CHEN Chen, SU Hongjun, et al. Local binary patterns and extreme learning machine for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7):3681-3693. [12] JIA Sen, LIN Zhijie, DENG Bin, et al. Cascade superpixel regularized gabor feature fusion for hyperspectral image classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(5):1638-1652. [13] LIU Bing, YU Anzhu, TAN Xiong, et al. Slow feature extraction for hyperspectral image classification[J]. Remote Sensing Letters, 2021, 12(5):429-438. [14] ROMASZEWSKI M, GLOMB P, CHOLEWA M. Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 121:60-76. [15] LIU Bing, YU Xuchu, YU Anzhu, et al. Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks[J]. Remote Sensing Letters, 2018, 9(12):1118-1127. [16] 刘冰, 余旭初, 张鹏强, 等. 联合空-谱信息的高光谱影像深度三维卷积网络分类[J]. 测绘学报, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578. LIU Bing, YU Xuchu, ZHANG Pengqiang, et al. Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578. [17] WANG Kexian, ZHENG Shunyi, LI Rui, et al. A deep double-channel dense network for hyperspectral image classification[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4):46-62. [18] XU Yonghao, ZHANG Liangpei, DU Bo, et al. Spectral-spatial unified networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10):5893-5909. [19] XUE Zhixiang, YU Xuchu, TAN Xiong, et al. Multiscale deep learning network with self-calibrated convolution for hyperspectral and LiDAR data collaborative classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-16. [20] XUE Zhixiang, TAN Xiong, YU Xuchu, et al. Deep hierarchical vision transformer for hyperspectral and LiDAR data classification[J]. IEEE Transactions on Image Processing, 2022, 31:3095-3110. [21] ZHENG Zhuo, ZHONG Yanfei, MA Ailong, at 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(8), 5612-5626. [22] ZHU Lin, CHEN Yushi, PEDRAM G, at al. Generative adversarial networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9):5046-5063. [23] QIN Anyong, SHANG Zhaowei, TIAN Jinyu, et al. Spectral-spatial graph convolutional networks for semisupervised hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 16(2):241-245. [24] 左溪冰, 刘冰, 余旭初, 等. 高光谱影像小样本分类的图卷积网络方法[J]. 测绘学报, 2021, 50(10):1358-1369. DOI:10.11947/j.AGCS.2021.20200155. ZUO Xibing, LIU Bing, YU Xuchu, et al. Graph convolutional network method for small sample classification of hyperspectral images[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(10):1358-1369. DOI:10.11947/j.AGCS.2021.20200155. [25] LIU Bing, YU Anzhu, YU Xuchu, et al. Deep multiview learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9):7758-7772. [26] 陶超, 阴紫薇, 朱庆, 等. 遥感影像智能解译:从监督学习到自监督学习[J]. 测绘学报, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089. TAO Chao, YIN Ziwei, ZHU Qing, et al. Remote sensing image intelligent interpretation:from supervised learning to self-supervised learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1122-1134. DOI:10.11947/j.AGCS.2021.20210089. [27] XUE Zhixiang, YU Xuchu, YU Anzhu, et al. Self-supervised feature learning for multimodal remote sensing image land cover classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-15. [28] HE Xin, CHEN Yushi, PEDRAM G. Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5):3246-3263. [29] WANG Haoyu, CHEN Yushi, CHEN C L P, et al. Hyperspectral image classification based on domain adversarial broad adaptation network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-13. [30] GAO Kuiliang, GUO Wenyue, YU Xuchu, et al. Deep induction network for small samples classification of hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13:3462-3477. |