A New Learning Paradigm for Foundation Model-Based Remote-Sensing Change Detection

Change detection (CD) is a critical task to observe and analyze dynamic processes of land cover. Although numerous deep-learning (DL)-based CD models have performed excellently, their further performance improvements are constrained by the limited knowledge extracted from the given labeled data. On the other hand, the foundation models that emerged recently contain a huge amount of knowledge by scaling up across data modalities and proxy tasks. In this article, we propose a bi-temporal adapter network (BAN), which is a universal foundation model-based CD adaptation framework aiming to extract the knowledge of foundation models for CD. The proposed BAN contains three parts, that is, the frozen foundation model (e.g., CLIP), bi-temporal adapter branch (Bi-TAB), and bridging modules between them. Specifically, BAN extracts general features through a frozen foundation model, which are then selected, aligned, and injected into Bi-TAB via the bridging modules. Bi-TAB is designed as a model-agnostic concept to extract task/domain-specific features, which can be either an existing arbitrary CD model or some hand-crafted stacked blocks. Beyond current customized models, BAN is the first extensive attempt to adapt the foundation model to the CD task. Experimental results show the effectiveness of our BAN in improving the performance of existing CD methods (e.g., up to 4.08% IoU improvement) with only a few additional learnable parameters. More importantly, these successful practices show us the potential of foundation models for remote-sensing (RS) CD. The code is available at
https://github.com/likyoo/BAN
and will be supported in our Open-CD.