Remote Sensing Image Change Detection Based on Low-Rank Representation

摘要：

　　In this paper we propose an unsupervised approach based on lowrank representation (LRR) for change detection in remote sensing images. Given a pair of remote sensing images obtained from the same area but in different time, the subtraction and logarithm ratio operators are firstly applied to obtain two difference images. Meanwhile the sparse part generated by LRR is also employed for acquiring another difference image, which can detect the change information. Afterwards, LRR is used again to obtain the low-rank part of these three difference images which can reflect the common characteristics. Finally k-means is performed on the low-rank part and thus the final result of change detection can be gained. Experimental results show the effectiveness and feasibility of the proposed method.

关键词： Change detection Remote sensing Low-rank representation K-means

作者: Yan Cheng Zhiguo Jiang Jun Shi Haopeng Zhang Gang Meng

作者单位: Image Processing Center, School of Astronautics, Beihang University, Beijing, China ;Beijing Key Lab Beijing Institute of Remote Sensing Information, Beijing, China

会议类型: 国际会议

会议名称: 9th Conference on Image and Graphics Technologies and Applications(IGTA2014)(第九届图像图形技术与应用学术会议)

会议地点: 北京

会议语种:英文

页码: 353-362

在线出版日期: 2014-06-01（万方平台首次上网日期，不代表论文的发表时间）