摘要
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。
The detection accuracy of traditional pixel-level change detection algorithms is seriously influenced by radiometric 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.
出处
《自动化学报》
EI
CSCD
北大核心
2008年第9期1040-1046,共7页
Acta Automatica Sinica
基金
国家自然科学基金(60472028)资助~~
关键词
变化检测
人造目标
几何结构
HOG描述子
Gabor纹理
Change detection, man-made object, geometric structure, histogram of oriented gradient （HOG） descriptor, Gabor texture
