Nonlocal patch similarity based heterogeneous remote sensing change detection

• Published in: Pattern recognition Vol. 109; p. 107598
• Main Authors: Sun, Yuli, Lei, Lin, Li, Xiao, Sun, Hao, Kuang, Gangyao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2021
• Subjects: Graph; Heterogeneous data; Nonlocal similarity; Unsupervised change detection; Nonlocal similarity; Unsupervised change detection; Graph; Heterogeneous data
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2020.107598

•An unsupervised change detection framework is proposed by constructing graphs based on nonlocal patch similarity to make heterogeneous data comparable.•The graphs are compared on the same domain to avoid the leakage of heterogeneous data.•The forward and backward change detection results are fused by distribution oriented way. Change detection of heterogeneous remote sensing images is an important and challenging topic, which has found a wide range of applications in many fields, especially in the emergency situation resulting from nature disaster. However, the difference in imaging mechanism of heterogeneous sensors makes it difficult to carry out a direct comparison of images. In this paper, we propose a new change detection method based on similarity measurement between heterogeneous images. The method constructs a graph for each patch based on the nonlocal patch similarity to establish a connection between heterogeneous data, and then measures the change level by measuring how much the graph structure of one image still conforms to that of the other image. The graph structures are compared in the same domain, so it can avoid the leakage of heterogeneous data and bring more robust change detection results. Experiments demonstrate the effective performance of the proposed nonlocal patch similarity based heterogeneous change detection method.