Multimodal Fusion Framework for Remote Sensing
Figure 1: Overview of the proposed multimodal intelligent fusion framework for remote sensing applications. The framework first processes multi-source remote sensing data (optical imagery, SAR, LiDAR, hyperspectral) via the Dynamic Resolution Input Strategy (DRIS) , which balances feature extraction accuracy and computational efficiency. Cross-modal semantic matching is then implemented through the Multi-scale Vision–language Alignment Mechanism (MS-VLAM) , which decomposes alignment into Object-level, Local-region-level, and Global-level granularities to strengthen visual-textual consistency. This framework supports a variety of downstream tasks, including land cover classification, disaster response, and urban management.
Paper context
Paper title: Multimodal Interpretation of Remote Sensing Images: Dynamic Resolution Input Strategy and Multi-scale Vision-Language Alignment Mechanism Abstract: Multimodal fusion of remote sensing images serves as a core technology for overcoming the limitations of single-source data and improving the accuracy of surface information extraction, which exhibits significant application value in fields such as environmental monitoring and urban planning. To address the deficiencies of existing methods, including the failure of fixed resolutions to balance efficiency and detail, as well as the lack of semantic hierarchy in single-scale alignment, this study proposes a Vision-language Model (VLM) framework integrated with two key innovations: the Dynamic Resolution Input Strategy (DRIS) and the Multi-scale Vision-language Alignment Mechanism (MS-VLAM).Specifically, the DRIS adopts a coarse-to-fine approach to adaptively allocate computational resources according to the complexity of image content, thereby preserving key fine-grained features while reducing redundant computational overhead. The MS-VLAM constructs a three-tier alignment mechanism covering object, local-region and global levels, which systematically captures cross-modal semantic consistency and alleviates issues of semantic misalignment and granularity imbalance.Experimental results on the RS-GPT4V dataset demonstrate that the proposed framework significantly improves the accuracy of semantic understanding and computational efficiency in tasks including image captioning and cross-modal retrieval. Compared with conventional methods, it achieves superior performance in evaluati Passages referencing this figure: 1 Research significance of the subject Figure 1: Overview of the proposed multimodal intelligent fusion framework for remote sensing applications.
