Classification of Hyperspectral Remote Sensing Image Based on Sparse Representation and Spectral Information

Plaza A, Benediktsson J A, Boardman J, et al.. Recent advances in techniques for hyperspectral image processing [J]. Remote Sensing of Environment, 2009, 113(9): 110-122.[2] 邸韡, 潘泉, 赵永强, 等. 高光谱图像波段子集模糊积分融合异常检测[J]. 电子与信息学报, 2008, 30(2): 267-271.Di Wei, Pan Quan, Zhao Yong-qiang, et al.. Anomaly target detection in hyperspectral imagery based on band subset fusion by fuzzy integral[J]. Journal of Electronics Information Technology, 2008, 30(2): 267-271.[3] 宋娟, 吴成柯, 张静, 等. 基于分类和陪集码的高光谱图像无损压缩[J]. 电子与信息学报, 2011, 33(1): 231-234.Song Juan, Wu Cheng-ke, Zhang Jing, et al.. Lossless compression of hyperspectral images based on classification and coset coding [J]. Journal of Electronics Information Technology, 2011, 33(1): 231-234.[4] Chan J C W and Paelinckx D. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery[J]. Remote Sensing of Environment, 2008, 112(6): 2999-3011.[5] Ham J, Chen Y, Crawford M, et al.. Investigation of the random forest framework for classification of hyperspectral data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 492-501.[6] Shahshahani B M and Landgrebe D A. The effect of unlabeled samples in reducing the small sample size problem and mitigating the hughes phenomenon[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(5): 1087-1095.[7] Breiman L. Random forests [J]. Machine Learning, 2001, 45(1): 5-32.[8] Olshausen B A and Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature, 1996, 381(6583): 607-609.[9] Iordache M D, Dias J M B, and Plaza A. Sparse unmixing of hyperspectral data. [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2014-2039.[10] Wright J, Ma Y, Mairal J, et al.. Sparse representations for computer vision and pattern recognition [J]. Proceedings of the IEEE, 2010, 98(6): 1031-1044.[11] 余慧敏, 方广有. 压缩感知理论在探地雷达三维成像中的应用[J]. 电子与信息学报, 2010, 32(1): 12-16.Yu Hui-min and Fang Guang-you. Research on compressive sensing based 3D imaging method applied to ground penetrating radar [J]. Journal of Electronics Information Technology, 2010, 32(1): 12-16.[12] 屈乐乐, 方广有, 杨天虹. 压缩感知理论在频率步进探地雷达偏移成像中的应用[J]. 电子与信息学报, 2011, 33(1): 21-26.Qu Le-le, Fang Guang-you, and Yang Tian-hong. The application of compressed sensing to stepped-frequency ground penetrating radar migration imaging [J]. Journal of Electronics Information Technology, 2011, 33(1): 21-26.[13] 孙玉宝, 韦志辉, 吴敏, 等. 稀疏性正则化的图像泊松去噪算法[J]. 电子学报, 2011, 39(2): 285-290.Sun Yu-bao, Wei Zhi-hui, Wu Min, et al.. Image poisson denoising using sparse representations [J]. Acta Elcetronica Sinica, 2011, 39(2): 285-290.[14] Raina R, Battle A, Lee H, et al.. Self-taught learning: transfer learning from unlabeled data[C]. International Conference on Machine Learning, Corvallis, 2007: 759-766. [15] Qiao Li-shan, Chen Song-can, and Tan Xiao-yang. Sparsity preserving projection with applications to face recognition [J]. Pattern Recognition, 2010, 43(1): 331-341.[16] Han Ya-hong, Wu Fei, Zhuang Yue-ting, et al.. Multi-label transfer learning with sparse representation[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(8): 1110-1121.[17] Aharon M, Elad M, and Bruckstein A. K-SVD: an algorithm for designing over-complete dictionaries for sparse representation [J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.[18] Mairal J, Bach F, Ponce J, et al.. Online learning for matrix factorization and sparse coding [J]. Journal of Machine Learning Research, 2010, 11(1): 19-60.[19] 童庆禧, 张兵, 郑兰芬. 高光谱遙感: 原理、技术与应用[M]. 北京: 高等教育出版社, 2006: 262-272.Tong Qing-xi, Zhang Bing, and Zheng Lan-fen. Hyperspectral Remote Sensing Theory, Technology and Applications [M]. Beijing: Higer Education Press, 2006: 262-272.[20] Breiman L. Bagging predictors [J]. Machine Learning, 1996, 24(2): 123-140.