A Change Detection Algorithm for Man-made Objects Based on Multi-temporal Remote Sensing Images
基于多时相遥感图像的人造目标变化检测算法

The detection accuracy of traditional pixel-level change detection algorithms is seriously influenced by ra- diometric difference,registration error and the determination of classification threshold for a different 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 the changed regions containing man-made objects are extracted by supervised sub-image classification.Then,a pixel-level change detection algorithm is developed to obtain quantitative detection results.Experimental results demonstrate the feasibility and effectiveness of the proposed algorithm.