Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which highresolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful representation for nonlinear relationships of deep neural networks, we introduce multiscale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods. Index Terms-Convolutional neural network (CNN), multiscale feature learning, pan-sharpening, remote sensing.
Yuan et al. REF proposed the multiscale and multidepth convolutional neural network for pansharpening of remote sensing images.
A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Computer Science Mathematics