Unsupervised Change Detection for Remote Sensing Images Based on Principal Component Analysis and Differential Evolution

This paper proposed a novel method for unsupervised change detection of remote sensing images using principal component analysis and differential evolution (PDECD). PDECD consists of two main steps. Firstly, an eigenvector space is generated by principal component analysis (PCA) of image blocks. Difference image is projected onto the eigenvector space to extract image local features, which is essentially composed of local smoothing feature and edge fidelity features. Then PDECD regards change detection as an optimal clustering problem and utilizes the differential evolution algorithm (DE) to search for the optimal change detection results without any priori knowledge. Compared with the existing methods, PDECD is not only robust to image noise, but also sensitive to small changed details. In addition, PDECD can avoid tracking to the local optima in change detection process and improve the detection performance due to the powerful global optimization capability of DE. Considering the image data belonging to two clusters cannot separated by sharp boundaries, so the Jm index of standard fuzzy clustering method is used as the objective function of DE. In order to improve the robustness and automatic detection capability of PDECD, control parameters of DE have been adjusted adaptively. Experiments conducted on real SAR and optical remote sensing images demonstrate the effectiveness of the proposed method.