Unsupervised Pan-Sharpening Network Incorporating Imaging Spectral Prior and Spatial-Spectral Compensation

Deep learning has achieved significant success in pan sharpening, but there are still two nonnegligible challenges. First, most of the existing methods rely on reduced-resolution training samples, limiting their performance when migrating from simulated to real-world scenes. Second, they pay insufficient attention to the imaging mechanism and the complexity and heterogeneity of remote sensing information, which leads to unclear representation of the relationships between images and the underutilization of the full-resolution features. In response to the abovementioned issues, this article presents an unsupervised pan-sharpening network incorporating imaging spectral prior and spatial-spectral compensation, named USCPNet. First, a structure-guided cross-attention (CA) residual (SCAR) block is constructed, deriving the desired high-resolution multispectral (HRMS) image guided by the panchromatic texture-structure features. A spectrally adaptive degradation network (SNet) coupling imaging spectral prior is then introduced, which characterizes the pixel-by-pixel spectral mapping between the HRMS image and the high-resolution panchromatic (HRPAN) image, to implement a precise spatial constraint driven by the imaging mechanism. In addition, given the difficulty of comprehensively extracting and integrating complementary features within an unsupervised framework through single-stream fusion, spatial-spectral joint progressive compensated (SSPC) stages are employed to achieve refined enhancement of the effective information in the predicted HRMS image through iterative rounds of residual fusion. Experiments conducted on Gaofen-1 (GF-1), Gaofen-2 (GF-2), and WorldView-2 (WV-2) satellite images reveal that the proposed USCPNet excels in spatial enhancement (SE) while preserving spectral fidelity. USCPNet also demonstrates advantages in specific applications, such as large-scale image fusion and vegetation index generation, compared with state-of-the-art methods.