Image and Signal Processing Methods

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

[+] Author Affiliations
Yuanchao Su, Jun Li

Sun Yat-Sen University, School of Geography and Planning, Guangzhou, Haizhu District 510275, China

Xu Sun, Lianru Gao, Bing Zhang

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, Haidian District 100094, China

J. Appl. Remote Sens. 10(4), 045018 (Nov 17, 2016). doi:10.1117/1.JRS.10.045018
History: Received April 1, 2016; Accepted October 24, 2016
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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.

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Citation

Yuanchao Su ; Xu Sun ; Lianru Gao ; Jun Li and Bing Zhang
"Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images", J. Appl. Remote Sens. 10(4), 045018 (Nov 17, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.045018


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