SAR Image Change Detection Based on Nonlocal Low-Rank Model and Two-Level Clustering

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Yuli Sun¹  Lin Lei¹  Xiao Li¹  Dongdong Guan¹  Gangyao Kuang¹ 

[1] College of Electronic Science, National University of Defense Technology, Changsha, China;

关键词: Low-rank modeling;    speckle reduction;    synthetic aperture radar (SAR) images;    two-level clustering;    unsupervised change detection (CD);   

DOI  :  10.1109/JSTARS.2019.2960518

来源: DOAJ

【 摘 要 】

Change detection (CD) has found a wide range of applications in many fields. In this article, we propose a novel nonlocal low-rank (NLR) based method for multitemporal synthetic aperture radar image CD. This method jointly exploits the powerful NLR-based despeckling and the effective cascade clustering. First, the NLR model is used to generate the difference image (DI), which consists of a patch grouping process and a low-rank minimizing process. Especially, the NLR minimization model contains a data fidelity term, which is based on the statistical distribution of speckle noise, and a regularization term, which uses the weighted nuclear norm. Then, the alternating direction methods of multipliers is introduced to solve this minimization problem. Second, after DI is generated, the principal component analysis is employed to extract the feature and a two-level clustering method is used to generate the final change map, which separates the intermediate class by using the neighbor information with Gaussian weighted distance. Experiment results demonstrate the effectiveness of the proposed method by comparing with some state-of-the-art methods.

【 授权许可】

Unknown