Dimensionality Reduction and Classification based on Ant Colony Algorithm for Hyperspectral Remote Sensing Image

This paper proposes a method of dimensionality reduction and classification based on ant colony algorithm for hyperspectral remote sensing image. The high-dimensional hyperspectral data space is decomposed into several low-dimensional data subspace by ant colony algorithm (ACA) in terms of the correlation between bands. Then principal component analysis is used in subspace to extract features, whereafter the classification of hyperspectral image is carried out by maximum likelihood classifier. The experiments show that comparing with the method of dimensionality reduction which doesn´t use ACA decomposition (i.e. standard PCA), the method proposed is more reasonable, and reserves more useful information, has the higher classification accuracy.