SARCLIP: A Vision Language Foundation Model for Semantic Understanding and Target Recognition in SAR Imagery

• URL: http://arxiv.org/abs/2510.22665v1
• Date: Sun, 26 Oct 2025 13:04:50 GMT
• Title: SARCLIP: A Vision Language Foundation Model for Semantic Understanding and Target Recognition in SAR Imagery
• Authors: Qiwei Ma, Zhiyu Wang, Wang Liu, Xukun Lu, Bin Deng, Puhong Duan, Xudong Kang, Shutao Li,
• Abstract summary: We introduce SARCLIP, the first vision language foundation model tailored for the SAR domain.<n>SARCLIP is trained using a contrastive vision language learning approach by domain transferring strategy.<n>Experiments on image-text retrieval and zero-shot classification tasks demonstrate the superior performance of SARCLIP.
• Score: 46.87845911116779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic Aperture Radar (SAR) has emerged as a crucial imaging modality due to its all-weather capabilities. While recent advancements in self-supervised learning and Masked Image Modeling (MIM) have paved the way for SAR foundation models, these approaches primarily focus on low-level visual features, often overlooking multimodal alignment and zero-shot target recognition within SAR imagery. To address this limitation, we construct SARCLIP-1M, a large-scale vision language dataset comprising over one million text-image pairs aggregated from existing datasets. We further introduce SARCLIP, the first vision language foundation model tailored for the SAR domain. Our SARCLIP model is trained using a contrastive vision language learning approach by domain transferring strategy, enabling it to bridge the gap between SAR imagery and textual descriptions. Extensive experiments on image-text retrieval and zero-shot classification tasks demonstrate the superior performance of SARCLIP in feature extraction and interpretation, significantly outperforming state-of-the-art foundation models and advancing the semantic understanding of SAR imagery. The code and datasets will be released soon.

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