Image-based retrieval in large Earth observation archives is challenging because one needs to navigate across thousands of candidate matches only with the query image as a guide. By using text as information supporting the visual query, the retrieval system gains in usability, but at the same time faces difficulties due to the diversity of visual signals that cannot be summarized by a short caption only. For this reason, as a matching-based task, cross-modal text-image retrieval often suffers from information asymmetry between texts and images. To address this challenge, we propose a Knowledge-aware Text-Image Retrieval (KTIR) method for remote sensing images. By mining relevant information from an external knowledge graph, KTIR enriches the text scope available in the search query and alleviates the information gaps between texts and images for better matching. Moreover, by integrating domain-specific knowledge, KTIR also enhances the adaptation of pre-trained vision-language models to remote sensing applications. Experimental results on three commonly used remote sensing text-image retrieval benchmarks show that the proposed knowledge-aware method leads to varied and consistent retrievals, outperforming state-of-the-art retrieval methods.

通过使用外部知识图谱挖掘相关信息，我们提出了一种适用于遥感图像的知识感知文本-图像检索（KTIR）方法，该方法丰富了搜索查询中可用的文本范围，缓解了文本和图像之间的信息差距，实现更好的匹配。实验结果表明，该方法在遥感文本-图像检索中表现出优异的检索能力，超越了现有的最先进方法。

远程感知影像的知识感知文本-影像检索