Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images

Heterogeneous change detection (HeCD) enables the identification of land-cover changes using remote sensing imagery obtained from different sensors. Most existing methods overly emphasize modality transformation and shared feature extraction to bridge the gap between heterogeneous images. While these strategies facilitate comparable representations, they tend to neglect the intrinsic characteristics of the changes themselves, which limits their effectiveness in complex scenarios. To overcome this limitation, we propose a change prior-guided image transformation model (CPIT) for unsupervised HeCD. Specifically, starting from the definition of change detection, we analyze the connections among pairwise object relationships, change labels, and change semantics, and then derive change semantic consistency and inconsistency rules solely from the inherent nature of the change detection problem, without relying on data-specific assumptions. These rules are subsequently encoded as change semantic consistency and inconsistency constraints, which, from the perspective of graph signal processing, correspond to low-pass and high-pass spectral properties of the change signals. Finally, by integrating these semantic constraints with sparsity priors and image transformation constraints, we formulate a more precise transformation model for HeCD. Solving this model produces change detection results that conform to the change priors, thereby improving the detection performance. The derivation, formulation, and utilization of change priors in this work offer valuable insights for broader change detection research. Extensive experiments on five datasets validate the effectiveness of CPIT. The code will be released at https://github.com/yulisun/CPIT.