CADUI: Cross-Attention-Based Depth Unfolding Iteration Network for Pansharpening Remote Sensing Images
Zhixuan Li, Jinjiang Li, Fan Zhang, Linwei Fan
Abstract
Pansharpening is an important technology for remote sensing imaging systems to obtain high-resolution multispectral (HRMS) images. It mainly obtains high-resolution multi-spectral (HRMS) images with uniform spectral distribution and rich spatial details by fusing low-resolution multi-spectral (LRMS) images and high-spatial-resolution panchromatic (PAN) images. Therefore, how to extract features completely and reconstruct images with high quality is critical to obtain ideal fusion images. In this paper, we propose a new pansharpening method, called the Cross Attention-based Depth Unfolding Iteration Network for Pan-sharpening remote sensing images (CADUI), which achieves the desired fusion effect by iteratively optimizing the deep prior regularization and combining it with a cross-attention mechanism. The network consists of two parts: optimized iterations of deep prior regularization (DEIN-Block) and cross-attention mechanism (CAFM-Block). Among them, DEIN-Block introduces the depth prior as an implicit regularization and improves the adaptability and representation ability of the relevant data of the reconstructed image through iteration. CAFM-Block realizes dual-branch fusion through cross-attention fusion and channel-attention fusion to achieve better fusion results. Simulation experiments and real experiments are carried out on the standard datasets QuikBird (QB) and WorldView-2 (WV2). Through quantitative comparison and qualitative analysis, it is proved that the method is superior to the existing methods.