CLOSP：遥感中SAR、MSI与文本的统一语义空间
CLOSP: A Unified Semantic Space for SAR, MSI, and Text in Remote Sensing
AI总结：
针对遥感检索局限于RGB数据的问题，提出CLOSP框架，利用文本桥接对齐SAR与光学图像，显著提升检索性能，并引入地理坐标增强特定任务能力。
摘要：
从庞大的卫星档案中检索相关图像对于灾害响应和长期气候监测等应用至关重要。然而，大多数文本到图像的检索系统仅限于RGB数据，未能利用其他传感器捕获的独特物理信息，例如合成孔径雷达（SAR）的全天候结构敏感性或光学多光谱数据中的光谱特征。为弥补这一差距，我们引入了CrisisLandMark，这是一个新的大规模语料库，包含超过647,000对Sentinel-1 SAR和Sentinel-2多光谱图像，并配有结构化的文本注释，涵盖土地覆盖、土地利用和危机事件，这些注释来自权威土地覆盖系统（CORINE和Dynamic World）以及危机特定来源。随后，我们提出了CLOSP（对比语言光学SAR预训练），这是一个新颖的框架，利用文本作为桥梁，将未配对的光学图像和SAR图像对齐到统一的嵌入空间中。我们的实验表明，CLOSP达到了新的最先进水平，在检索nDGC@1000上比现有模型提高了54%。此外，我们发现，这种统一训练策略通过间接交互从光学领域转移丰富的语义知识，克服了SAR图像解释的固有困难。进一步地，GeoCLOSP将地理坐标集成到我们的框架中，在通用性和特异性之间实现了强大的权衡：虽然CLOSP擅长一般语义任务，但GeoCLOSP成为检索与位置相关的危机事件和稀有地理特征的专门专家。这项工作强调了整合多样化的传感器数据和地理背景对于释放遥感档案全部潜力的重要性。
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@1000 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.
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