An unsupervised change detection approach for remote sensing image using principal component analysis and genetic algorithm

The novel approach presented in this paper aims for unsupervised change detection applicable and adaptable to remote sensing images. This is achieved based on a combination of principal component analysis (PCA) and genetic algorithm (GA). The PCA is firstly applied to difference image to enhance the change information, and the significance index F is computed for selecting the principal components which contain predominant change information based on Gaussian mixture model. Then the unsupervised change detection is implemented and the resultant optimal binary change detection mask is obtained by minimizing a mean square error (MSE) based fitness function using GA. We apply the proposed and the state-of-the-art change detection methods to ASTER and QuickBird data sets, meanwhile the extensive quantitative and qualitative analysis of change detection results manifests the capability of the proposed approach to consistently produce promising results on both data sets without any priori assumptions.