Research Papers

Dimensionality reduction of hyperspectral imagery using improved locally linear embedding

[+] Author Affiliations
Guangyi Chen, Shen-En Qian

Canadian Space Agency

J. Appl. Remote Sens. 1(1), 013509 (March 19, 2007). doi:10.1117/1.2723663
History: Received November 8, 2006; Revised March 14, 2007; Accepted March 14, 2007; March 19, 2007; Online March 19, 2007
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Abstract

In this paper, we study the Locally Linear Embedding (LLE) for nonlinear dimensionality reduction of hyperspectral data. We improve the existing LLE in terms of both computational complexity and memory consumption by introducing a spatial neighbourhood window for calculating the k nearest neighbours. The improved LLE can process larger hyperspectral images than the existing LLE and it is also faster. We conducted experiments of endmember extraction to assess the effectiveness of the dimensionality reduction methods. Experimental results show that the improved LLE is better than PCA and the existing LLE in identifying endmembers. It finds more endmembers than PCA and the existing LLE when the Pixel Purity Index (PPI) based endmember extraction method is used. Also, better results are obtained for detection.

© 2007 Society of Photo-Optical Instrumentation Engineers

Citation

Guangyi Chen and Shen-En Qian
"Dimensionality reduction of hyperspectral imagery using improved locally linear embedding", J. Appl. Remote Sens. 1(1), 013509 (March 19, 2007). ; http://dx.doi.org/10.1117/1.2723663


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