An object-based change detection approach using uncertainty analysis for VHR images

Abstract

This paper proposes an object-based approach to supervised change detection using uncertainty analysis for very high resolution (VHR) images. First, two temporal images are combined into one image by band stacking. Then, on the one hand, the stacked image is segmented by the statistical region merging (SRM) to generate segmentation maps; on the other hand, the stacked image is classified by the support vector machine (SVM) to produce a pixel-wise change detection map. Finally, the uncertainty analysis for segmented objects is implemented to integrate the segmentation map and pixel-wise change map at the appropriate scale and generate the final change map. Experiments were carried out with SPOT 5 and QuickBird data sets to evaluate the effectiveness of proposed approach. The results indicate that the proposed approach often generates more accurate change detection maps compared with some methods and reduces the effects of classification and segment scale on the change detection accuracy. The proposed method supplies an effective approach for the supervised change detection for VHR images.