[1] 余旭初,冯伍法,林丽霞.高光谱——遥感测绘的新机遇[J].测绘科学技术学报,2006,23(2):130-132.[2] 贺霖,潘泉,邸韡,等.高光谱图像目标检测进展[J].电子学报,2009,37(9):2016-2024.[3] 杨国鹏.基于机器学习方法的高光谱影像分类研究[D].郑州:信息工程大学,2010.[4] 吴波,张良培,李平湘.基于支持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):312-318.[5] LI KUN-LUN, HUANG HOU-KUAN, TIAN SHENG-FENG, et al. Improving one-class SVM for anomaly detection [C]// Proceedings of 2003 International Conference on Machine Learning and Cybernetics. Washington, DC: IEEE Computer Society, 2003: 3077-3081.[6] 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.[7] TAX D, DUIN R. Data domain description using by support vectors [C]// Proceedings of 1999 the European Symposium on Artificial Neural Networks. Brussel: D-Facto, 1999: 251-256.[8] SCHLKOPF B, PLATT J C, SHAWE-TAYLOR J T, et al. Estimating the support of a high-dimensional distribution [J]. Neural Computation, 2001, 13(7): 1443-1471.[9] SCHLKOPF B, SMOLA J, WILLIAMSON R C, et al. New support vector algorithms [J]. Neural Computation, 2000, 12(5): 1207-1245.[10] 白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008:18.[11] TAX D, DUIN R. Support vector domain description [J]. Pattern Recognition Letters, 1999, 20(11/12/13): 1191-1199.[12] UNNTHORSSON R, RUNARSSON R T, JOHNSON T M. Model selection in one class nu-SVMs using RBF kernels [C]// Proceedings of the 16th International Conference on Condition Monitoring and Diagnostic Engineering Management. Vaxjo, Sweden: Vxj University Press, 2003: 1054-1060.[13] 万余庆,谭克龙,周日平,等.高光谱遥感应用研究[M].北京:科学出版社,2006:123-125.[14] 张新乐,张树文,李颖,等.基于光谱角度匹配方法提取黑土边界[J].光谱学与光谱分析,2009,29(4):1056-1059. |