Kernel simplex growing algorithm for hyperspectral endmember extraction

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[1] Zhao, Liaoying
[2] Zheng, Junpeng
[3] Li, Xiaorun
[4] Wang, Lijiao
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Li, X. (lxr@zju.edu.cn) | 1600年 / SPIE卷 / 08期
关键词：
In order to effectively extract endmembers for hyperspectral imagery where linear mixing model may not be appropriate due to multiple scattering effects; this paper extends the simplex growing algorithm (SGA) to its kernel version. A new simplex volume formula without dimension reduction is used in SGA to form a new simplex growing algorithm (NSGA). The original data are nonlinearly mapped into a high-dimensional space where the scatters can be ignored. To avoid determining complex nonlinear mapping; a kernel function is used to extend the NSGA to kernel NSGA (KNSGA). Experimental results of simulated and real data prove that the proposed KNSGA approach outperforms SGA and NSGA. © 2014 SPIE;
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