Change Dino: A Unified Transformer-Based Framework For Object-Level Change Detection and Segmentation in Remote Sensing Imagery

In the realm of remote sensing change detection, deep learning-based pixel-level methods have shown commendable accuracy and speed. However, due to the difficulty in distinguishing between each changed object and the high matching accuracy required, there are still limitations in practical applications. To address these issues, we propose Change DINO, a novel unified object-level change detection and segmentation framework and the inaugural Transformer-based object-level change detection framework, which leverages the Hierarchical Temporal Fusion Module (HTFM) with dual branches to extract change features from bi-temporal images, integrating these features into the Transformer's encoder-decoder and segmentation branches. Experimental results show that compared to other pixel-level (including Transformer-based) change detection methods, Change DINO exhibits superior performance even on pixel-level evaluation strategy, achieving F1 score improvements of 5.09% and 10.31% compared with Transformer-based methods. This capability significantly mitigates the limitations inherent in pixel-level detection, showcasing Change DINO's substantial potential for diverse applications in change detection tasks.