Cross-Domain Difference Representation Learning for Unsupervised Heterogeneous Change Detection

In the remote sensing community, heterogeneous images captured by various observation platforms usually exhibit different visual appearances and statistical distribution characteristics, making it difficult to detect land surface changes through direct comparison. Most existing change detection (CD) methods aim to extract change information by transforming heterogeneous images into a common image domain or high dimensional feature space for change analysis, neglecting the deep mining of image domain-invariant features. To overcome this challenge, a novel unsupervised cross-domain difference representation learning (CDRL) framework is proposed for heterogeneous CD, including an image translation network and a CD network. First, the image translation network combines within-domain self-reconstruction and cross-domain image translation with CD constraints to enable joint optimization, thereby constructing a style-independent, content-comparable feature space for obtaining difference information between heterogeneous images. Subsequently, patch-based samples with reliable labels are selected by analyzing the difference information through thresholding. On this basis, a simple yet efficient CD network is established by partially reusing the content feature extraction module and incorporating the difference information fusion module. This design enables the network to effectively learn the semantics of both changed and unchanged pixels, thus accurately identifying the ground changes. Extensive experimental results on five different types of datasets demonstrate the effectiveness of the proposed method, showing remarkable improvements over state-of-the-art approaches in terms of accuracy and efficacy. The code and dataset are available at https://github.com/OMEGA-RS/CDRL.