Tuning a SAM-Based Model with Multi-Cognitive Visual Adapter to Remote Sensing Instance Segmentation

The Segment Anything Model (SAM), a foundational model designed for promptable segmentation tasks, demonstrates exceptional generalization capabilities, making it highly promising for natural scene image segmentation. However, SAM's lack of pretraining on massive remote sensing images and its interactive structure limit its automatic mask prediction capabilities. In this paper, a Multi-Cognitive SAM-Based Instance Segmentation Model (MC-SAM SEG) is introduced to employ SAM on remote sensing domain. The SAM-Mona encoder utilizing the Multi-cognitive Visual Adapter (Mona) is conducted to facilitate SAM's transfer learning in remote sensing applications. The proposed method named MC-SAM SEG extracts high-quality features by fine-tuning the SAM-Mona encoder along with a feature aggregator. Subsequently, a pixel decoder and transformer decoder are designed for prompt-free mask generation and instance classification. The comprehensive experiments are conducted on the HRSID and WHU datasets for instance segmentation tasks on Synthetic Aperture Radar (SAR) images and optical remote sensing images respectively. The evaluation results indicate the proposed method surpasses other deep learning algorithms and verify its effectiveness and generalization.