RAH-VLA: Resolution-Adaptive Hierarchical Vision-Language Alignment for Multimodal Remote Sensing Understanding

Abstract

Multimodal vision-language modeling has emerged as a promising paradigm for remote sensing (RS) image understanding. However, existing methods are limited by fixed-resolution visual processing and single-scale vision-language alignment, making it difficult to simultaneously preserve fine-grained details and maintain semantic consistency across different spatial granularities. To address these challenges, we propose RAH-VLA, a Resolution-Adaptive Hierarchical Vision-Language Alignment framework for multimodal remote sensing understanding. Specifically, a Dynamic Resolution Input Strategy (DRIS) is developed to enable resolution-adaptive visual representations, while a Multi-scale Vision-Language Alignment Mechanism (MS-VLAM) is introduced to establish hierarchical semantic correspondence across object-level, region-level, and global-level representations. Extensive experiments on multiple remote sensing benchmarks demonstrate that RAH-VLA consistently improves image captioning, visual grounding, and cross-modal reasoning performance while reducing computational redundancy. Qualitative analyses further illustrate the effectiveness of the proposed resolution-adaptive perception and hierarchical vision-language alignment mechanisms, as well as the cross-modality generalization capability of the proposed framework. Overall, RAH-VLA provides an effective and scalable solution for multimodal remote sensing interpretation, offering a practical pathway toward efficient and semantically robust RS vision-language models.