%0 Journal Article
%T Unsupervised compressive sensing of change area in remote sensing images<br>遥感图像变化区域的无监督压缩感知
%A Yang Meng
%A Zhang Gong
%A <br>杨萌
%A 张弓
%J 中国图象图形学报
%D 2011
%I 
%X Traditional remote sensing image change detection approaches based on structure features are usually limited by imaging stability. In this paper, we introduce a new method for unsupervised change detection in remote sensing images using compressive sensing (CS) based on the image inherent sparse structures. For this algorithm, a large collection of image patches is projected onto high dimensional spaces through redundant dictionary, giving an adaptive sparse representation per each image patch. A random matrix is taken as measurement matrix to realize feature space dimension reduction. Then, the final change detection is realized by clustering the feature vector space using the fuzzy C-mean clustering(FCM)algorithm, achieving the reconstruction of change regional information. The experimental results demonstrate that the proposed algorithm has good change detection results both in contour and region and has a good robustness.
%K change detection
%K remote sensing image
%K compressive sensing (CS)
%K fuzzy C-means (FCM) clustering<br>变化检测
%K 遥感图像
%K 压缩感知(CS)
%K 模糊C均值(FCM)聚类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=3D120F67FFBE1E377A6274B740D3C814&yid=9377ED8094509821&vid=7801E6FC5AE9020C&iid=708DD6B15D2464E8&sid=6756FAD7BC937C79&eid=5B4FE8EC29FFFACE&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=21