SDPNet: A Deep Network for Pan-Sharpening With Enhanced Information Representation
Han Xu, Jiayi Ma, Zhenfeng Shao, Hao Zhang, Junjun Jiang, Xiaojie Guo
Abstract
In this article, we propose a surface- and deep-level constraint-based pan-sharpening network, termed SDPNet, to address the pan-sharpening problem. Focusing on the two primary goals of pan-sharpening, i.e., spatial and spectral information preservations, we first design two encoder-decoder networks to extract deep-level features from two types of source images, in addition to surface-level characteristics, as the enhanced information representation. The unique feature maps that characterize the unique information in source images can be obtained through the deep-level feature extraction. We further design a pan-sharpening network with densely connected blocks to strengthen feature propagation and reduce parameter number, where the unique feature maps are utilized to efficiently constrain the similarity between the pan-sharpened result and the ground truth, thus avoiding information distortion. Both qualitative and quantitative comparisons on the reduced-resolution and full-resolution source images demonstrate the advantages of our method over state-of-the-art methods. Our code is publicly available at https://github.com/hanna-xu/SDPNet.