SARCLIP: A Vision Language Foundation Model for Semantic Understanding and Target Recognition in SAR Imagery
This work addresses the need for better semantic understanding and target recognition in SAR imagery, which is crucial for applications like all-weather surveillance, but it is incremental as it adapts existing vision-language methods to a specific domain.
The paper tackles the problem of limited multimodal alignment and zero-shot target recognition in Synthetic Aperture Radar (SAR) imagery by introducing SARCLIP, a vision-language foundation model trained on a new large-scale dataset, which significantly outperforms state-of-the-art models in tasks like image-text retrieval and zero-shot classification.
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.