PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model

• URL: http://arxiv.org/abs/2412.12737v2
• Date: Tue, 30 Sep 2025 07:27:49 GMT
• Title: PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model
• Authors: Yuqing Wang, Zhongling Huang, Shuxin Yang, Hao Tang, Xiaolan Qiu, Junwei Han, Dingwen Zhang,
• Abstract summary: PolSAR data presents unique challenges due to its rich and complex characteristics.<n>Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used.<n>Most feature extraction networks for PolSAR are small, limiting their ability to capture features effectively.<n>We propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), an enhanced Segment Anything Model (SAM) that integrates domain-specific scattering characteristics and a novel prompt generation strategy.
• Score: 83.35198885088093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: PolSAR data presents unique challenges due to its rich and complex characteristics. Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used. However, these formats often face issues related to usability, interpretability, and data integrity. Most feature extraction networks for PolSAR are small, limiting their ability to capture features effectively. To address these issues, We propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), an enhanced Segment Anything Model (SAM) that integrates domain-specific scattering characteristics and a novel prompt generation strategy. PolSAM introduces Microwave Vision Data (MVD), a lightweight and interpretable data representation derived from polarimetric decomposition and semantic correlations. We propose two key components: the Feature-Level Fusion Prompt (FFP), which fuses visual tokens from pseudo-colored SAR images and MVD to address modality incompatibility in the frozen SAM encoder, and the Semantic-Level Fusion Prompt (SFP), which refines sparse and dense segmentation prompts using semantic information. Experimental results on the PhySAR-Seg datasets demonstrate that PolSAM significantly outperforms existing SAM-based and multimodal fusion models, improving segmentation accuracy, reducing data storage, and accelerating inference time. The source code and datasets will be made publicly available at https://github.com/XAI4SAR/PolSAM.

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