Special Section on Airborne Hyperspectral Remote Sensing of Urban Environments

Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification

[+] Author Affiliations
Jianwei Gao

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

Qian Du

Mississippi State University, Mississippi State, Mississippi 39762, United States

Lianru Gao

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

Xu Sun

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

Bing Zhang

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

J. Appl. Remote Sens. 8(1), 085094 (Aug 13, 2014). doi:10.1117/1.JRS.8.085094
History: Received March 23, 2014; Revised July 8, 2014; Accepted July 21, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Jianwei Gao ; Qian Du ; Lianru Gao ; Xu Sun and Bing Zhang
"Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification", J. Appl. Remote Sens. 8(1), 085094 (Aug 13, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.085094


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