Object-Based Change Detection for VHR Images Based on Multiscale Uncertainty Analysis

Scale is of great significance in image analysis and interpretation. In order to utilize scale information, multiscale fusion is usually employed to combine change detection (CD) results from different scales. However, CD results from different scales are usually treated independently, which ignores the scale contextual information. To overcome this drawback, this letter introduces a novel object-based change detection (OBCD) technique for unsupervised CD in very high-resolution (VHR) images by incorporating multiscale uncertainty analysis. First, two temporal images are stacked and segmented using a series of optimal segmentation scales ranging from coarse to fine. Second, an initial CD result is obtained by fusing the pixel-based CD result and OBCD result based on Dempter-Shafer (DS) evidence theory. Third, multiscale uncertainty analysis is implemented from coarse scale to fine scale by support vector machine classification. Finally, a CD map is generated by combining all the available information in all the scales. The experimental results employing SPOT5 and GF-1 images demonstrate the effectiveness and superiority of the proposed approach.