13 August 2014 Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification
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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.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jianwei Gao, Qian Du, Lianru Gao, Xu Sun, Bing Zhang, "Ant colony optimization-based supervised and unsupervised band selections for hyperspectral urban data classification," Journal of Applied Remote Sensing 8(1), 085094 (13 August 2014). https://doi.org/10.1117/1.JRS.8.085094 . Submission:
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