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For hyperspectral image processing, dimensionality reduction is an important step, which has direct impact on hyperspectral image classification accuracy. Unsupervised band selection is an important means of data dimensionality reduction. This paper presents an ant colony optimization (ACO) algorithm based hyperspectral image band selection method (ACO-BS). First, four kinds of distance are used to...
In this paper, ant colony optimization (ACO) is applied to hyperspectral band selection. The objective is to select a small band subset such that classification accuracy can be maintained or even improved. The ACO-based band selection technique in this research is independent of any classifier, resulting in lower computational cost. Both supervised (i.e., Jeffries-Matusita distance) and unsupervised...
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