A Change Detection Algorithm for Man-made Objects Based on Multi-temporal Remote Sensing Images

The detection accuracy of traditional pixel-level change detection algorithms is seriously influenced by radiometric difference, misregistration error and the determination of classification threshold for difference image, and it is difficult to differentiate the true changes of interest from various kinds of detected changes. Therefore, a novel two-step change detection algorithm combining feature-level and pixel-level techniques is proposed to detect changes of man-made objects in multi-temporal remote sensing images. Large-size images are divided into overlapping sub-images, and candidate changed regions containing man-made objects are extracted by supervised sub-image classification. Then, pixel-level change detection algorithm is developed to obtain quantitative detection results. Experimental results demonstrate the feasibility and effectiveness of the proposed algorithm.