Hyperboloid-Embedded Siamese Network for Change Detection in Remote Sensing Images

Change detection (CD) is a significant branch of remote sensing image analysis. Its main distinction from general semantic segmentation lies in the input of bitemporal images. Recent CD methods have primarily focused on Euclidean space, disregarding the hidden non-Euclidean details due to the high imaging altitude and complex scenes in remote sensing imagery. This limitation hampers the model's performance. In recent years, hyperbolic space has gradually been introduced into deep learning as a classical non-Euclidean space. To explore the potential impact of hyperbolic information in CD, a hyperbolic-embedding-based Siamese network is proposed in this article. Specifically, we propose a hyperbolic similarity attention mechanism that can map deep features from bitemporal images into hyperbolic space, then establish the relationship between bitemporal features based on the hyperbolic distance, fully mining non-Euclidean information, and fusing features from both branches to enhance feature cohesiveness. Furthermore, we design a differential feature enhanced module in the decoder, which utilizes differential operations at multiple scales to highlight the essence of each layer's features and improve feature richness. Experimental results on two public very-high-resolution CD datasets demonstrate that the proposed network achieves better detection accuracy than other state-of-the-art CD methods.