FAME: Fusion of Alignment and Multiview Enhancement for Remote Sensing Image–Text Retrieval

Contemporary advancements in Earth observation technologies have generated substantial data resources for remote sensing image retrieval applications. However, existing models exhibit limitations in extracting local features from image–text pairs and establishing effective connections between local and global information. Furthermore, these models lack fine-grained alignment capabilities between different modalities. To address these challenges, we propose FAME, which integrates four key components: a progressive masking (ProgMask) module, an OmniView fusion (OVF) module, a global feature attention with multiview feature alignment (GFA-MFA) module, and regional clustering alignment loss (RCA-Loss). The ProgMask module employs a progressive training strategy with alternating masking of image and text modalities to enhance cross-modal learning robustness. The OVF module employs omni-view attention mechanisms with adaptive weighting to dynamically focus on different local semantic regions of remote sensing images and corresponding textual descriptions. The GFA-MFA module enhances global feature capture through selective filtering while ensuring precise multiview feature alignment. These modules work synergistically to achieve fine-grained alignment between multiscale local and global features across modalities. In addition, RCA-Loss optimizes intraclass feature clustering while minimizing interclass confusion through class center distance optimization and enhanced feature discrimination. Experimental validation on three benchmark datasets (RSITMD, RSICD, and UCM-captions) demonstrates that FAME achieves superior performance compared with existing state-of-the-art methods in remote sensing image–text retrieval (RSITR) tasks.