Progressive Modality-Alignment for Unsupervised Heterogeneous Change Detection

Change detection (CD) based on heterogeneous images is of great importance in some applications, such as disaster monitoring and damage assessment. However, due to the huge modality discrepancy in heterogeneous images, it is difficult to accurately detect the changed regions. In this article, we analyze the interference of modality alignment and changed areas to each other and propose a progressive modality alignment-based unsupervised CD model for heterogeneous images. Specifically, the modality alignment is achieved in an iterative manner, which can improve the detection accuracy progressively. To reduce the influence of modality discrepancy and the changed regions to each other, a pseudo-label self-learning strategy is designed, where the pseudo-labels learned by the model itself are used to act as guidance of CD, and they are, in turn, refined by the proposed progressive model. Experimental results on different real heterogeneous images verify the effectiveness and robustness of the proposed method.