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Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources

Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources

来源:Arxiv_logoArxiv
英文摘要

Retrieving relevant imagery from vast satellite archives is crucial for applications like disaster response and long-term climate monitoring. However, most text-to-image retrieval systems are limited to RGB data, failing to exploit the unique physical information captured by other sensors, such as the all-weather structural sensitivity of Synthetic Aperture Radar (SAR) or the spectral signatures in optical multispectral data. To bridge this gap, we introduce CrisisLandMark, a new large-scale corpus of over 647,000 Sentinel-1 SAR and Sentinel-2 multispectral images paired with structured textual annotations for land cover, land use, and crisis events harmonized from authoritative land cover systems (CORINE and Dynamic World) and crisis-specific sources. We then present CLOSP (Contrastive Language Optical SAR Pretraining), a novel framework that uses text as a bridge to align unpaired optical and SAR images into a unified embedding space. Our experiments show that CLOSP achieves a new state-of-the-art, improving retrieval nDGC by 54% over existing models. Additionally, we find that the unified training strategy overcomes the inherent difficulty of interpreting SAR imagery by transferring rich semantic knowledge from the optical domain with indirect interaction. Furthermore, GeoCLOSP, which integrates geographic coordinates into our framework, creates a powerful trade-off between generality and specificity: while the CLOSP excels at general semantic tasks, the GeoCLOSP becomes a specialized expert for retrieving location-dependent crisis events and rare geographic features. This work highlights that the integration of diverse sensor data and geographic context is essential for unlocking the full potential of remote sensing archives.

Daniele Rege Cambrin、Lorenzo Vaiani、Giuseppe Gallipoli、Luca Cagliero、Paolo Garza

测绘学地球物理学

Daniele Rege Cambrin,Lorenzo Vaiani,Giuseppe Gallipoli,Luca Cagliero,Paolo Garza.Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources[EB/OL].(2025-07-14)[2025-07-25].https://arxiv.org/abs/2507.10403.点此复制

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