Adaptive Spatial Structure-Aware and Spectral Gradient Structure Tensor-Guided Model for Pansharpening

In this article, we propose a novel adaptive spatial structure-aware and spectral gradient structure tensor-guided model (AS(3)GSTM) for pansharpening, which realizes the process of fusing the low-resolution multispectral (LRMS) image and the paired panchromatic (Pan) image to output the high-resolution multispectral (HRMS) image. Specifically, based on the basic spectral fidelity term between HRMS and LRMS obtained from the spatial degradation model for spectral fidelity, we also enforce the radiometric ratio-guided high-frequency detail fidelity term between HRMS, LRMS, and Pan for high-frequency detail fidelity. Moreover, considering that the HRMS image and the Pan image actually not only have strong spatial structure similarities, but also differ from each other, we further propose a novel Pan-guided adaptive spatial structure-aware prior term for the HRMS image to guide the fusion process. Besides, we particularly exploit the structure tensor of the spectral gradient of HRMS for simultaneously spectral-spatial prior modeling, and propose a novel spectral gradient-guided structure tensor total variation prior term for the HRMS image. Subsequently, we design an efficiently alternating algorithm to optimize the proposed AS(3)GSTM model. Finally, lots of fusion experiments comprehensively validate the superiority of AS(3)GSTM.