Exploring Text-Guided Information Fusion Through Chain-of-Reasoning for Pansharpening

Pan-sharpening aims to enhance the spatial resolution of low-resolution multispectral (LRMS) images by integrating high-frequency information from a corresponding texture-rich panchromatic (PAN) image, while maintaining the spectral integrity of the LRMS image. Although text-guided multi-modal learning has made considerable strides in the natural image domain, its potential to pan-sharpening remains underexplored, primarily due to the limited availability of multi-modal remote sensing datasets. To this end, we construct an entirely new pan-sharpening framework by making efforts from three key aspects: (1) text-equipped multi-modal data collection through chain-of-reasoning, (2) large model prior-driven multi-modal information fusion, and (3) visual information interaction through prompt engineering, leveraging textual information to guide the pan-sharpening process within a multi-modal fusion framework. We initially utilize the generic large language model priors to generate descriptive captions for MS images, forming a multi-modal pan-sharpening dataset. By integrating super-resolved imagery and segmentation maps generated by segment anything, we apply Chain-of-Thought (CoT) prompting to generate spatially focused captions across diverse satellite datasets. These captions enhance visual features and provide high-level contextual information, improving semantic understanding for pan-sharpening. Building on the aforementioned multi-modal data, we tailor two text-guided information fusion modules: Textual Enhancement Block (TEB) standing on large model prior and Textual Modulated Block (TMB) utilizing text information to effectively guide and refine the pan-sharpening fusion process. Extensive experiments on multiple satellite datasets demonstrate that our proposed framework outperforms state-of-the-art methods, highlighting its effectiveness and superior performance in pan-sharpening.