基于多时相遥感图像的人造目标变化检测算法

传统的像素级变化检测方法的检测性能受到以下因素的严重制约: 图像辐射差异、配准误差和差异图像分类门限的选取, 并且难以从检测信息中提取出关键的变化. 本文针对遥感图像中人造目标的变化检测问题, 提出了一种综合特征级和像素级的两步变化检测算法. 首先将大幅多时相遥感图像分成一系列子图像对, 采用有监督子图像对分类方法, 提取人造目标变化的感兴趣区域, 然后采用像素级变化检测算法对感兴趣区域进行变化检测, 得到定量的检测结果. 实验结果表明了该算法的可行性和有效性.

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 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.