Self-supervised change detection of heterogeneous images based on difference algorithms

The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods.