A Progressive Spectral Correction and Spatial Compensation Network for Pansharpening

Abstract

Pansharpening aims to produce a high resolution multispectral image by fusing a panchromatic image with a low-resolution multispectral image. Current pansharpening methods often overlook the significant modality differences between source images and lack interaction between them, resulting in spatial-spectral distortions. To address these issues, we proposed a novel progressive spectral correction and spatial compensation network for pansharpening. The network comprises a spectral correction branch, a spatial compensation branch, and a spectral-spatial fusion (SSF) branch. In the spectral correction branch, we designed a local spectral reinforcement (LSR) module and a global spectral rectification (GSR) module to keep the spectral fidelity. The LSR module is designed to reinforce the unique local information from different kinds of spectral features, while the GSR module captures long-range dependency and rectifies the spectral features with a cross-attention mechanism. In the spatial compensation branch, we designed a multiscale dilated adaptive feature extraction module guided by spectral and spatial attention to extract useful spatial details, and the details are progressively compensated into the SSF branch to better keep spatial fidelity. The SSF branch is designed to interact with spectral correction branch and spatial compensation branch to mitigate the modal difference and progressively optimize the spectral-spatial information. Comprehensive experiments show that the proposed method outperforms current state-of-the-art pansharpening methods.