Object-based change detection of very high-resolution remote sensing images incorporating multiscale uncertainty analysis by fusing pixel-based change detection

Pixel-based change detection (PBCD) is imperfect because it lacks spatial correlation and can cause misdetection and salt and pepper noise. Comparatively, object-based change detection (OBCD) is dependent on the accuracy of the segmentation scale, where over-segmentation or under-segmentation of the image objects reduce accuracy. The fusion of PBCD and OBCD maps has great potential in dealing with spectral variability and texture complexity in very high-resolution (VHR) remote sensing images. It is difficult to solve the problem of uncertainty, which is caused by the inaccuracy of the multiple-change maps. Evidence theory based on Dempster–Shafer (DS) theory is an effective method for modeling uncertainty and taking advantage of multiple pieces of evidence. In this study, we proposed a scale-driven CD method incorporating DS evidence theory and majority voting rule to generate CD by combining multiscale OBCD results and PBCD results. Experiments carried out in four different regions using the Gaofen-2 imagery were used to test the proposed approach. We conducted numerous experiments to compare the proposed approach with prevalent CD approaches. Based on the results, the proposed approach achieves the best performance because it combines the benefits of pixel-based and object-based methods.