Sparse-Constrained Adaptive Structure Consistency-Based Unsupervised Image Regression for Heterogeneous Remote-Sensing Change Detection

Abstract

Change detection of heterogeneous multitemporal satellite images is an important and challenging topic in remote sensing. Since the imaging mechanisms of heterogeneous sensors are different, it is not possible to directly compare heterogeneous images to detect changes as in the homogeneous images. To address this challenge, we propose an unsupervised image regression-based change detection method based on the structure consistency. The proposed method first adaptively constructs a similarity graph to represent the structure of a pre-event image, then uses the graph to translate the pre-event image to the domain of the post-event image, and then computes the difference image. Finally, a superpixel-based Markovian segmentation model is designed to segment the difference image into changed an