Detecting changes without comparing images: Rules induced change detection in heterogeneous remote sensing images

Abstract

Heterogeneous change detection (HCD) is crucial for monitoring surface changes using various remote sensing data, especially in disaster emergency response and environmental monitoring. To facilitate the comparability of heterogeneous images, previous methods are devoted to designing various complex transformation functions to transfer heterogeneous images into a common domain for comparison. As a result, the performance of HCD is constrained by the accuracy and robustness of these transformation functions. Unlike existing comparison-based HCD methods that rely on complex transformations and feature alignments between heterogeneous images, this paper proposes an unsupervised rules-induced energy model (RIEM) that detects changes by independently analyzing intra-image relationships, without explicitly comparing the heterogeneous images. This frees HCD from the complicated and challenging transformations and interactions between heterogeneous images. Specifically, we first establish the connections between the class relationships (same/different) and change labels (changed/unchanged) of pairwise superpixels, and then derive six rules for determining the change label of each superpixel, which enables detecting changes by considering only the intra-image relationships within each image, without inter-image comparisons. Then, we build an energy-based model to release the ability of rules to identify changes, which implements four types of energy loss functions. Remarkably, since the rules used in the energy model are derived based on the nature of change detection problem, the proposed RIEM is highly robust to imaging conditions. Extensive experiments on seven datasets demonstrate the efficacy of RIEM in detecting changes from heterogeneous images. The code is released at https://github.com/yulisun/RIEM.