Nonlocal patch similarity based heterogeneous remote sensing change detection

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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a new change detection method based on similarity measurement between heterogeneous images, which constructs a graph for each patch to establish a connection between heterogeneous data and measures the change level by comparing graph structures, achieving robust change detection results without leakage of heterogeneous data.