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Journal of Applied Remote Sensing

Kernel simplex growing algorithm for hyperspectral endmember extraction
Author(s): Liaoying Zhao; Junpeng Zheng; Xiaorun Li; Lijiao Wang
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Paper 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.

Paper Details

Date Published: 10 July 2014
PDF: 15 pages
J. Appl. Remote Sens. 8(1) 083594 doi: 10.1117/1.JRS.8.083594
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Liaoying Zhao, Hangzhou Dianzi Univ. (China)
Junpeng Zheng, Hangzhou Dianzi Univ. (China)
Xiaorun Li, Zhejiang Univ. (China)
Lijiao Wang, Zhejiang Univ. (China)

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