ChangeBind: A Hybrid Change Encoder for Remote Sensing Change Detection
This work addresses change detection in remote sensing, which is incremental as it combines local and global features to improve accuracy.
The paper tackles the problem of remote sensing change detection by addressing limitations of CNNs and transformers in capturing long-range dependencies and subtle changes, proposing a hybrid Siamese-based framework that achieves state-of-the-art performance on two challenging datasets.
Change detection (CD) is a fundamental task in remote sensing (RS) which aims to detect the semantic changes between the same geographical regions at different time stamps. Existing convolutional neural networks (CNNs) based approaches often struggle to capture long-range dependencies. Whereas recent transformer-based methods are prone to the dominant global representation and may limit their capabilities to capture the subtle change regions due to the complexity of the objects in the scene. To address these limitations, we propose an effective Siamese-based framework to encode the semantic changes occurring in the bi-temporal RS images. The main focus of our design is to introduce a change encoder that leverages local and global feature representations to capture both subtle and large change feature information from multi-scale features to precisely estimate the change regions. Our experimental study on two challenging CD datasets reveals the merits of our approach and obtains state-of-the-art performance.