MVAFG: Multiview Fusion and Advanced Feature Guidance Change Detection Network for Remote Sensing Images

In recent years, change detection (CD) methods have faced challenges in being applied to various types of remote sensing datasets and related research fields, particularly in the domain of CD in remote sensing images. While convolutional neural networks (CNNs) have significantly advanced CD in remote sensing images, they struggle with modeling long-distance dependencies between image pairs, leading to poor recognition of semantically similar objects with different features. Meanwhile, transformer technology has gained widespread popularity for global applications, but it lacks in extracting local features effectively. Current approaches typically rely on single or dual-branch network structures for mining change-related features in remote sensing images, yet they still lack in extracting both local and global features comprehensively. To address these issues, this article proposes a triple-branch network combining transformer and CNN, comprising CNN, transformer, and channel feature-guided branch. These branches extract and fuse three types of change features from both global and local perspectives. Importantly, the channel feature-guided branch is introduced to capture continuous and detailed change relationship features, thus enhancing the model's change discrimination ability. Experimental results on three datasets (LEVIR-CD, WHU-CD, and GZ-CD) demonstrate the superior performance of the model over state-of-the-art methods.