CHANGE DETECTION FOR HIGH-RESOLUTION REMOTE SENSING IMAGERY BASED ON MULTI-SCALE SEGMENTATION AND FUSION

Change detection techniques for remote sensing images are increasingly applied to many fields, such as disaster monitoring, vegetation coverage analysis and so on. How to improve the accuracy of detection has been a critical topic that confuse the researchers for a long time. In this paper, a method combining multiscale segmentation and fusion for high-resolution images is presented. The strategy of multiscale segmentation is to segment the same image several times under different scales, and then extract the features of objects. After, the features are used as inputs of change detection. The final results are achieved by decision-level fusion. The experiments show that, comparing with other typical methods, the method proposed in this paper has a superior performance in change detection for high-resolution images.