  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.
