A Multi-Scale Densely Deep Learning Method for Pansharpening

Pansharpening aims to produce a higher resolution multi-spectral (HRMS) image by fusing the spectral information in lower resolution multispectral (LRMS) image and the spatial information in corresponding high resolution panchromatic (PAN) image. In this work, we propose a multi-scale densely deep learning based pansharpening method. Following an end-to-end learning architecture, the proposed deep neural network contains three modules: 1) a parallel multi-scale convolutional layer is used to extract multiscale features of PAN image; 2) a global identity branch structure is adopted to preserve spectral structures; and 3) a dense learning block is integrated to improve the spectral-spatial expressive power. Compared with other state-of-the-art methods, experimental results obtained with our proposed method achieve high pansharpening quality in visualization and quantification.