Ant Colony Optimization Algorithm for Feature Selection and Classification of Multispectral Remote Sensing Image

In classification of a multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if we only consider the image´s spectral feature or texture feature alone. In this paper, we present a new approach by applying the Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to classify the multispectral remote sense image. Using the combination of the spectral and texture features obtained by ACO in classification always produces a better accuracy.