Kernel simplex growing algorithm for hyperspectral endmember extraction

Liaoying Zhao; Junpeng Zheng; Xiaorun Li; Lijiao Wang

doi:10.1117/1.JRS.8.083594

10 July 2014 Kernel simplex growing algorithm for hyperspectral endmember extraction

Liaoying Zhao, Junpeng Zheng, Xiaorun Li, Lijiao Wang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083594 (July 2014). https://doi.org/10.1117/1.JRS.8.083594

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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Liaoying Zhao, Junpeng Zheng, Xiaorun Li, and Lijiao Wang "Kernel simplex growing algorithm for hyperspectral endmember extraction," Journal of Applied Remote Sensing 8(1), 083594 (10 July 2014). https://doi.org/10.1117/1.JRS.8.083594

Published: 10 July 2014

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