Research Papers

Kernel simplex growing algorithm for hyperspectral endmember extraction

[+] Author Affiliations
Liaoying Zhao

Hangzhou Dianzi University, Institute of Computer Application Technology, Hangzhou 310018, China

Junpeng Zheng

Hangzhou Dianzi University, Institute of Computer Application Technology, Hangzhou 310018, China

Xiaorun Li

Zhejiang University, College of Electrical Engineering, Hangzhou 310027, China

Lijiao Wang

Zhejiang University, College of Electrical Engineering, Hangzhou 310027, China

J. Appl. Remote Sens. 8(1), 083594 (Jul 10, 2014). doi:10.1117/1.JRS.8.083594
History: Received February 12, 2014; Revised May 22, 2014; Accepted June 11, 2014
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Abstract.  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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Liaoying Zhao ; Junpeng Zheng ; Xiaorun Li and Lijiao Wang
"Kernel simplex growing algorithm for hyperspectral endmember extraction", J. Appl. Remote Sens. 8(1), 083594 (Jul 10, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083594


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