Multi-Scale-and-Depth Convolutional Neural Network for Remote Sensed Imagery Pan-Sharpening

Pan-sharpening is a fundamental and significant task in the field of remote sensed imagery fusion, which demands fusion of panchromatic and multi-spectral images with the rich information accurately preserved in both spatial and spectral domains. In this paper, to overcome the drawbacks of traditional pan-sharpening methodologies, we employed the advanced concept of deep learning to propose a Multi-Scale-and-Depth Convolutional Neural Network (MSDCNN) as an end-to-end pan-sharpening model. By the results of a large number of quantitative and visual assessments, the qualities of images fused by the proposed network have been confirmed superior to compared state-of-the-art methods.