遥感图像的多模态解释：动态分辨率输入策略与多尺度视觉-语言对齐机制
RAH-VLA: Resolution-Adaptive Hierarchical Vision-Language Alignment for Multimodal Remote Sensing Understanding
AI总结：
本文提出动态分辨率输入策略与多尺度视觉-语言对齐机制，提升遥感图像多模态解释的准确性和效率。
摘要：
多模态融合的遥感图像作为一种核心技术，用于克服单一数据源的局限性，提高表面信息提取的准确性，在环境监测和城市规划等领域具有显著的应用价值。为了解决现有方法的不足，包括固定分辨率在效率和细节之间的失衡以及单尺度对齐中语义层次的缺失，本研究提出了一种集成两个关键创新的视觉-语言模型（VLM）框架：动态分辨率输入策略（DRIS）和多尺度视觉-语言对齐机制（MS-VLAM）。具体而言，DRIS采用由粗到细的方法，根据图像内容的复杂性自适应分配计算资源，从而在保留关键细粒度特征的同时减少冗余计算开销。MS-VLAM构建了一个涵盖对象、局部区域和全局层次的三级对齐机制，系统地捕捉跨模态的语义一致性，并缓解语义错位和粒度不平衡的问题。在RS-GPT4V数据集上的实验结果表明，所提出的框架在图像描述和跨模态检索任务中显著提高了语义理解和计算效率。与传统方法相比，在图像描述的BLEU-4和CIDEr评估指标以及跨模态检索的R@10指标上均实现了更优的性能。该技术框架为构建高效且稳健的多模态遥感系统提供了一种新方法，奠定了理论基础，并为智能遥感解释的工程应用提供了技术指导。
英文摘要：
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.
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