SAM-Based Efficient Feature Integration Network for Remote Sensing Change Detection: A Case Study on Macao Sea Reclamation

Sea reclamation expands land resources along coastal areas and supports sustainable urban growth. Sea reclamation monitoring is a significant application of remote sensing change detection (RSCD). Segment anything model (SAM) supports zero-shot image segmentation, but lacks general features of remote sensing images (RSIs), making it difficult to directly apply to RSCD. To utilize the visual recognition capabilities of SAM for improving RSCD, we propose an SAM-based efficient feature integration network (EFI-SAM). Random fourier features adaptor (RFFA) is utilized to enhance Mobile SAM’s capability for extracting general features from complex RSIs, resulting in adaptive Mobile SAM, which serves as the feature extractor of EFI-SAM. To effectively integrate remote sensing difference features, long-range spatial features integration module (LSFI) is designed to fuse the long-range spatial correlation of bi-temporal semantic features. Cross-dimensional contextual information aggregation decoder (CCIA) is then designed to effectively aggregate the multidimensional change information. Furthermore, we collected Macao RSIs through GEE to create a dataset, named Macao Land Change Detection (MLCD) dataset, which contains 10 000 images of size $256 \times 256$. Experiments indicate that EFI-SAM achieves the highest metrics, with F1 of 80.75%, 75.86%, 82.86%, and 64.74% in MLCD, CLCD, SYSU-CD, and S2Looking, respectively. In addition to its high accuracy, it is noteworthy that EFI-SAM is a lightweight network with only 5.83 M parameter.