Pansharpening Based on Multiresolution Panchromatic Feature Guidance

Pansharpening, as a critical image fusion technique, aims to synthesize high-resolution multispectral (HRMS) images by integrating panchromatic (PAN) images with low-resolution multispectral (LRMS) data, thereby enhancing spatial details while maintaining spectral fidelity. Current methodologies exhibit limitations in comprehensively modeling inter-feature dependencies, often leading to suboptimal fusion performance characterized by spectral distortion or insufficient spatial enhancement. To address these challenges, we propose a novel multi-resolution PAN-guided pansharpening framework that systematically coordinates feature extraction, texture refinement, and spectral-spatial reconstruction. The proposed methodology is initiated with the development of a multi-resolution feature extraction network, founded on the spatial frequency Transformer module, which is designed to comprehensively extract spatial feature information at varying scales of both PAN and multispectral (MS) images. Subsequently, to effectively constrain and optimise the extracted spatial feature information, an interactive self-attentive texture injection module is employed to further extract detailed texture features that are more compatible with the spectral information of the MS image. These texture features are then injected into the MS reconstruction network in a step-by-step manner to guide the sharpening process of the MS image, while progressively up-sampling the size of the MS image to the PAN image to achieve more refined spatial features. Finally, the integration of multi-resolution feature information connected by residuals in the MS reconstruction network is achieved by means of an adaptive channel self-attention module. This module models the dependencies between feature maps in the channel dimension, and ultimately generates HRMS images. Experimental validation results demonstrate that the proposed method achieves excellent qualitative and quantitative evaluation results. Notably, it effectively reduces spatial blurring and edge distortion, significantly enhancing the visual effect and detail representation of the image.