[1] |
Chang C I. Hyperspectral Imaging: Techniques for Spectral Detection and Classification [M]. New York: Plenum, 2003. |
[2] |
|
[3] |
|
[4] |
Lin Yurong, Wang Qiang. Hyperspectral image classification based on adaptive weight coefficient based on kernel method[J]. Infrared and Laser Engineering, 2011, 40(12): 2535- 2539. (in Chinese) 林玉荣, 王强. 基于自适应权系数核方法的超光谱图像分 类[J]. 红外与激光工程, 2011, 40 (12): 2535-2539. |
[5] |
|
[6] |
Jolliffe L.T. Principal Component Analysis[M]. 2nd ed. New York: Springer, 2002. |
[7] |
|
[8] |
Sam T R, Lawrence K S. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000, 290 (5500): 2323-2326. |
[9] |
|
[10] |
Tenenbaum J B, De Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323. |
[11] |
|
[12] |
Bachmann C M, Ainsworth T L, Fusina R A. Exploiting manifold geometry in hyperspectral imagery [J]. IEEE Trans Geosci Remote Sens, 2005, 43(3): 441-454. |
[13] |
|
[14] |
Wei Feng, He Mingyi, Mei Shaohui. Hyperspectral data feature extraction using spatial coherence based neighborhood preserving embedding [J]. Infrared and Laser Engineering, 2012, 41(5): 1249-1254. (in Chinese) 魏峰, 何明一, 梅少辉. 空间一致性邻域保留嵌入的高光谱 数据特征提取[J]. 红外与激光工程, 2012, 41(5): 1249-1254. |
[15] |
Cover T, Hart P. Nearest neighbor pattern classification [J]. IEEE Trans on Inf Theory, 1967, 13(1): 21-27. |
[16] |
|
[17] |
Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Trans on Geosci Remote Sens, 2004, 42(8): 1778-1790. |
[18] |
|
[19] |
Chang C I, Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery [J]. IEEE Trans Geosci Remote Sens, 2004, 42(3): 608-619. |