Structure Consistency-Based Graph for Unsupervised Change Detection With Homogeneous and Heterogeneous Remote Sensing Images

Change detection (CD) of remote sensing (RS) images is one of the important problems in earth observation, which has been extensively studied in recent years. However, with the development of RS technology, the specific characteristics of remotely sensed images, including sensor characteristics, resolutions, noises, and distortions in imagery, make the CD more complex. In this article, we propose a structure consistency-based method for CD, which detects changes by comparing the structures of two images, rather than comparing the pixel values of images. Because the image structure is imaging modality-invariant and not sensitive to noise, illumination, and other interference factors, the proposed method can be applied to a variety of CD scenarios and has strong robustness. Structural comparison is realized by constructing and mapping an improved nonlocal patch-based graph (NLPG) to avoid the data leakage of two images. First, we demonstrate the effectiveness of the method in homogeneous and heterogeneous CD, which shows that the proposed method can be used as a unified CD framework. Second, we extend the method to the heterogeneous CD with multichannel synthetic aperture radar (SAR) image, which can provide a reference for future research as the heterogeneous CD with multichannel SAR is rarely studied. Third, through the decomposition and in-depth analysis of NLPG, we modify the graph construction process, structure difference calculation, and the difference image fusion to make it more robust and accurate. Experiments on six scenarios 12 data sets demonstrate the effectiveness of the proposed method.

Automated summary

This article proposes a structure consistency-based method for change detection in remote sensing images. By comparing the structures of two images instead of pixel values, the method demonstrates strong robustness and applicability to various scenarios. Additionally, the method shows effectiveness in both homogeneous and heterogeneous change detection, as well as in the seldom-studied case of heterogeneous change detection with multichannel synthetic aperture radar (SAR) images. Through analysis and improvements on the nonlocal patch-based graph (NLPG), the method is made more accurate and robust.