[1] 刘正春, 曾永年, 何丽丽, 等. 基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用[J]. 遥感技术与应用, 2012, 27(2): 159-167. LIU Zhengchun, ZENG Yongnian, HE Lili, et al. Method of Normalized Multiple Endmember Spectral Mixture Analysis and Its Application[J]. Remote Sensing Technology and Application, 2012, 27(2): 159-167. [2] 陈述彭, 童庆禧, 郭华东. 遥感信息机理研究[M]. 北京: 科学出版社, 1998. CHEN Shupeng, TONG Qinxi, GUO Huadong. Research on Mechanism of Remote Sensing Information[M]. Beijing: Science Press, 1998. [3] 王旭红, 郭建明, 贾百俊, 等. 元胞自动机的遥感影像混合像元分类[J]. 测绘学报, 2008, 37(1): 42-48. WANG Xuhong, GUO Jianming,JIA Baijun, et al. Mixed Pixels Classification of Remote Sensing Images Based on Cellular Automata[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(1): 42-48. [4] SOMERS B, ASNER G P, TITS L, et al. Endmember Variability in Spectral Mixture Analysis: A Review[J]. Remote Sensing of Environment, 2011, 115(7): 1603-1616. [5] 吴波, 熊助国. 基于光谱最佳尺度分割特征的高光谱混合像元分解[J]. 测绘学报, 2012, 41(2): 205-212. WU Bo,XIONG Zhuguo.Unmixing of Hyperspectral Mixture Pixels Based on Spectral Multiscale Segemented Features[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 205-212. [6] ZARE A, HO K C. Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability during Spectral Unmixing[J]. IEEE Signal Processing Magazine, 2014, 31(1): 95-104. [7] BEDINI E.Mapping Lithology of the Sarfartoq Carbonatite Complex,Southern West Greenland, Using HyMap Imaging Spectrometer Data[J]. Remote Sensing of Environment, 2009, 113(6): 1208-1219. [8] 丁建丽, 姚远. 干旱区绿洲典型地物MESMA模拟分解与验证[J]. 地球信息科学学报, 2013, 15(3): 452-460. DING Jianli, YAO Yuan. Research on Pixel Unmixing of Typical Surface Features in Oasis Based on the MESMA Model[J]. Journal of Geo-Information Science, 2013, 15(3): 452-460. [9] BACHMANN C M, AINSWORTH T L, FUSINA R A. Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2786-2803. [10] 吴波, 袁春. 非线性混合像元分解的可视化分析与评价[J]. 中国图象图形学报, 2010, 15(1): 167-173. WU Bo, YUAN Chun. Visualized Analysis and Evaluation of Nonlinear Unmixing the Mixed Pixels[J]. Journal of Image and Graphics, 2010, 15(1): 167-173. [11] BROADWATER J, BANERJEE A. A Generalized Kernel for Areal and Intimate Mixtures[C]//Proceedings of the 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Reykjavik: IEEE, 2010: 1-4. [12] BROADWATER J, BANERJEE A. Mapping Intimate Mixtures Using An Adaptive Kernel-based Technique[C]//Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Lisbon: IEEE, 2011: 1-4. [13] PLAZA J, PLAZA A. Spectral Mixture Analysis of Hyperspectral Scenes using Intelligently Selected Training Samples[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(2): 371-375. [14] ALTMANN Y, DOBIGEON N, TOURNERET J Y, et al. Nonlinear Unmixing of Hyperspectral Images using Radial Basis Functions and Orthogonal Least Squares[C]//Proceedings of IEEE International Conference Geoscience and Remote Sensing Symposium. Vancouver: IEEE, 2011: 1151-1154. [15] LICCIARDI G A, DEL F F. Pixel Unmixing in Hyperspectral Data by Means of Neural Networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4163-4172. [16] ALTMANN Y, HALIMI A, DOBIGEON N, et al. Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery[J]. IEEE Transactions on Image Processing, 2012, 21(6): 3017-3025. [17] VLADIMIR N V. The Nature of Statistical Learning Theory[M]. New York: Springer, 1998. [18] BOVOLO F,BRUZZONE L,CARLIN L.A Novel Technique for Subpixel Image Classification Based on Support Vector Machine[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2983-2999. [19] 李慧, 王云鹏, 李岩, 等. 基于SVM和PWC的遥感影像混合像元分解[J]. 测绘学报, 2009, 38(4): 318-323. LI Hui, WANG Yunpeng, LI Yan, et al. Unmixing of Remote Sensing Images Based on Support Vector Machines and Pairwise Coupling[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(4): 318-323. [20] HSU C W, LIN C J. A Comparison of Methods for Multiclass Support Vector Machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425. [21] 李慧, 王云鹏, 李岩, 等. 基于形态学和支持向量的遥感图像混合像元分解[J]. 遥感技术与应用, 2009, 24(1): 114-119. LI Hui, WANG Yunpeng, LI Yan, et al. Unmixing Remote Sensing Imagery Based on Morphology and Support Vector Machines[J]. Remote Sensing Technology and Application, 2009, 24(1): 114-119. [22] TANG J, WANG L, MYINT S W. Improving Urban Classification Through Fuzzy Supervised Classification and Spectral Mixture Analysis[J]. International Journal of Remote Sensing, 2007, 28(18): 4047-4063. [23] PONTIUS Jr R G, SHUSAS E, MCEACHERN M. Detecting Important Categorical Land Changes While Accounting for Persistence[J]. Agriculture, Ecosystems & Environment, 2004, 101(2-3): 251-268. [24] WILLMOTT C J, MATSUURA K. On the Use of Dimensioned Measures of Error to Evaluate the Performance of Spatial Interpolators[J]. International Journal of Geographical Information Science, 2006, 20(1): 89-102. [25] PONTIUS Jr R G, CHEUK M L. A Generalized Cross-tabulation Matrix to Compare Soft-classified Maps at Multiple Resolutions[J]. International Journal of Geographical Information Science, 2006, 20(1): 1-30. [26] CHAPELLE O, VAPNIK V, BOUSQUET O, et al. Choosing Multiple Parameters for Support Vector Machines[J]. Machine Learning, 2002, 46(1-3): 131-159. [27] 吴涛. 核函数的性质、方法及其在障碍检测中的应用[D]. 长沙: 国防科学技术大学, 2003. WU Tao. Kernels' Properties, Tricks and Its Applications on Obstacle Detection[D]. Changsha: National University of Defense Technology, 2003. |