Feature Selection and Classification Based on Ant Colony Algorithm for Hyperspectral Remote Sensing Images

Abstract

This paper proposes a method of feature selection and classification based on ant colony algorithm for hyperspectral remote sensing image. After all features are randomly projected on a plane, each ant stochastically selects a feature on the plane firstly, and then decides which route to be selected in terms of the criterion function among features. Whereafter the feature combination is formed. At last, using combination feature, the classification of AVIRIS image is carried out by maximum likelihood classifier. In order to verify the effectiveness of this algorithm, the approach is compared with the classical suboptimal search technique, using AVIRIS images as a data set. Experimental results prove the processing that based on ant colony algorithm is more effective and is fit for the band selection of hyperspectral image.