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Journal of Applied Remote Sensing

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images
Author(s): Yuanchao Su; Xu Sun; Lianru Gao; Jun Li; Bing Zhang
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Paper Abstract

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a “distance” factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

Paper Details

Date Published: 17 November 2016
PDF: 19 pages
J. Appl. Rem. Sens. 10(4) 045018 doi: 10.1117/1.JRS.10.045018
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Yuanchao Su, Sun Yat-Sen Univ. (China)
Xu Sun, Institute of Remote Sensing and Digital Earth (China)
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Jun Li, Sun Yat-Sen Univ. (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)

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