Remote Sensing Pan-Sharpening via Cross-Spectral–Spatial Fusion Network

Pan-sharpening is a technique used to create high-resolution multispectral (HRMS) images by merging low-resolution multispectral (LRMS) images with corresponding high-resolution panchromatic (PAN) images. Despite achieving state-of-the-art performance, existing panchromatic sharpening networks based on deep-learning (DL) methods suffer from spectral distortion and insufficient spatial texture enhancement. To address this challenge, this letter introduces a novel cross-spectral–spatial fusion network (CSSFN) for pan-sharpening remote sensing images. The network utilizes a cross-spectral–spatial attention block (CSSAB) to extract both the spectral information of the MS branch and the spatial information of the PAN branch. The spectral and spatial feature representations of remote sensing images are then progressively enhanced, which improves the fusion process and generates multispectral images with high spatial resolution. Our network outperforms other pan-sharpening methods on two publicly available datasets, as demonstrated by extensive experiments, yielding state-of-the-art results.