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

Yuanchao Su; Xu Sun; Lianru Gao; Jun Li; Bing Zhang

doi:10.1117/1.JRS.10.045018

17 November 2016 Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

Yuanchao Su, Xu Sun, Lianru Gao, Jun Li, Bing Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 4, 045018 (November 2016). https://doi.org/10.1117/1.JRS.10.045018

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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Yuanchao Su, Xu Sun, Lianru Gao, Jun Li, and Bing Zhang "Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images," Journal of Applied Remote Sensing 10(4), 045018 (17 November 2016). https://doi.org/10.1117/1.JRS.10.045018

Published: 17 November 2016

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