Style-Transfer-Based Unsupervised Change Detection From Heterogeneous Images

Heterogeneous images are captured through different wavelength bands, providing rich and complementary information for change detection (CD), and domain transformation has emerged as a popular and effective solution. However, existing domain-transformation-based CD methods overly rely on the quality of reconstructed features, making them appear inadequate for practical complex scenarios. In this article, we propose a style-transfer-based CD (STCD) method through unsupervised learning. STCD improves the quality and enhances the robustness of the reconstructed images by simultaneously employing a cautious labeling strategy and classification. Specifically, we initially convert the two heterogeneous images provided into a shared domain by constructing a convolutional autoencoder based on adaptive instance normalization, which improves the quality of reconstructed features and mitigates data heterogeneity. Furthermore, we extract some significant pixel pairs based on fuzzy local information cmeans to reduce the overreliance on reconstructed features. Then, we propose a dynamic superpixel-based label assignment rule to increase the reliable pseudo-labels employed in training a binary classifier. Finally, STCD achieves great CD results even with poor reconstruction quality. Experimental results conducted on four heterogeneous datasets demonstrate the effectiveness of STCD over other related CD methods.