PR-CLIP: Cross-Modal Positional Reconstruction for Remote Sensing Image–Text Retrieval
Jihong Guan, Yulou Shu, Wengen Li, Zihan Song, Yichao Zhang · Remote Sensing · 2025
With the development of satellite technology, remote sensing images have become increasingly accessible, making multi-modal remote sensing retrieval increasingly important. However, most existing methods rely on global visual and textual features to compute similarity, ignoring the positional correspondence between image regions and textual descriptions. To address this issue, we propose a novel cross-modal retrieval model named PR-CLIP, which leverages a cross-modal positional information reconstruction task to learn position-aware correlations between modalities. Specifically, PR-CLIP first uses a cross-modal positional information extraction module to extract the complementary features between images and texts. Then, the unimodal positional information filtering module filters out the complementary information from the unimodal features to generate embeddings for reconstruction. Finally, the cross-modal positional information reconstruction module reconstructs the unimodal embeddings of the images and texts based on the complete embeddings of the opposite modality, guided by a cross-modal positional consistency loss to ensure reconstruction quality. During the inference stage of retrieval, PR-CLIP directly calculates the similarity between the unimodal features without executing the modules of the reconstruction task. By combining the advantages of dual-stream and single-stream models, PR-CLIP achieves a good balance between performance and efficiency. Extensive experiments on multiple public datasets demonstrated the effectiveness of PR-CLIP.