RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensing

• URL: http://arxiv.org/abs/2509.02273v2
• Date: Wed, 01 Oct 2025 16:11:04 GMT
• Title: RS-OOD: A Vision-Language Augmented Framework for Out-of-Distribution Detection in Remote Sensing
• Authors: Chenhao Wang, Yingrui Ji, Yu Meng, Yunjian Zhang, Yao Zhu,
• Abstract summary: Out-of-distribution (OOD) detection represents a critical challenge in remote sensing applications.<n>We propose RS-OOD, a novel framework that leverages remote sensing-specific vision-language modeling to enable robust few-shot OOD detection.<n>Our approach introduces three key innovations: spatial feature enhancement that improved scene discrimination, a dual-prompt alignment mechanism, and a confidence-guided self-training loop.
• Score: 19.743431031185736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) detection represents a critical challenge in remote sensing applications, where reliable identification of novel or anomalous patterns is essential for autonomous monitoring, disaster response, and environmental assessment. Despite remarkable progress in OOD detection for natural images, existing methods and benchmarks remain poorly suited to remote sensing imagery due to data scarcity, complex multi-scale scene structures, and pronounced distribution shifts. To this end, we propose RS-OOD, a novel framework that leverages remote sensing-specific vision-language modeling to enable robust few-shot OOD detection. Our approach introduces three key innovations: spatial feature enhancement that improved scene discrimination, a dual-prompt alignment mechanism that cross-verifies scene context against fine-grained semantics for spatial-semantic consistency, and a confidence-guided self-training loop that dynamically mines pseudo-labels to expand training data without manual annotation. RS-OOD consistently outperforms existing methods across multiple remote sensing benchmarks and enables efficient adaptation with minimal labeled data, demonstrating the critical value of spatial-semantic integration.

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