Customized SAM 2 for Referring Remote Sensing Image Segmentation

• URL: http://arxiv.org/abs/2503.07266v1
• Date: Mon, 10 Mar 2025 12:48:29 GMT
• Title: Customized SAM 2 for Referring Remote Sensing Image Segmentation
• Authors: Fu Rong, Meng Lan, Qian Zhang, Lefei Zhang,
• Abstract summary: We propose RS2-SAM 2, a novel framework that adapts SAM 2 to RRSIS by aligning the adapted RS features and textual features.<n>We also introduce a text-guided boundary loss to optimize segmentation boundaries by computing text-weighted gradient differences.<n> Experimental results on several RRSIS benchmarks demonstrate that RS2-SAM 2 achieves state-of-the-art performance.
• Score: 21.43947114468122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Although Segment Anything Model 2 (SAM 2) has shown remarkable performance in various segmentation tasks, its application to RRSIS presents several challenges, including understanding the text-described RS scenes and generating effective prompts from text descriptions. To address these issues, we propose RS2-SAM 2, a novel framework that adapts SAM 2 to RRSIS by aligning the adapted RS features and textual features, providing pseudo-mask-based dense prompts, and enforcing boundary constraints. Specifically, we first employ a union encoder to jointly encode the visual and textual inputs, generating aligned visual and text embeddings as well as multimodal class tokens. Then, we design a bidirectional hierarchical fusion module to adapt SAM 2 to RS scenes and align adapted visual features with the visually enhanced text embeddings, improving the model's interpretation of text-described RS scenes. Additionally, a mask prompt generator is introduced to take the visual embeddings and class tokens as input and produce a pseudo-mask as the dense prompt of SAM 2. To further refine segmentation, we introduce a text-guided boundary loss to optimize segmentation boundaries by computing text-weighted gradient differences. Experimental results on several RRSIS benchmarks demonstrate that RS2-SAM 2 achieves state-of-the-art performance.

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