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Journal of Applied Remote Sensing

Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification
Author(s): Jianwei Gao; Qian Du; Lianru Gao; Xu Sun; Bing Zhang
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Paper Abstract

Band selection (BS), which selects a subset of original bands that contain the most useful information about objects, is an important technique to reduce the dimensionality of hyperspectral data. Dimensionality reduction before hyperspectral data classification can reduce redundancy information and even improve classification accuracy. We propose BS algorithms based on an ant colony optimization (ACO) in conjunction with objective functions such as the supervised Jeffries–Matusita distance and unsupervised simplex volume. Moreover, we propose to use a small number of selected pixels for BS in order to reduce computational cost in the unsupervised BS. In this experiment, the proposed algorithms were applied to three airborne hyperspectral datasets including urban scenes, and the results demonstrated that the ACO-based BS could find a better combination of bands than the widely used sequential forward search-based BS. It was acceptable to use a few pixels to achieve comparable BS performance with our method.

Paper Details

Date Published: 13 August 2014
PDF: 17 pages
J. Appl. Rem. Sens. 8(1) 085094 doi: 10.1117/1.JRS.8.085094
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Jianwei Gao, Institute of Remote Sensing and Digital Earth (China)
Qian Du, Mississippi State Univ. (United States)
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Xu Sun, Institute of Remote Sensing and Digital Earth (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)

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