Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network

Pansharpening and super-resolution (SR) methods share the same target to improve the spatial resolution of images. Based on this similarity, we propose and develop a novel pansharpening algorithm that is guided by a deep SR convolutional neural network. The proposed framework comprises three components: an SR process, a progressive pansharpening process, and a high-pass residual module. Specifically, the SR process extracts inner spatial detail that is present in multispectral images. Then, progressive pansharpening is used as a detailed pansharpening process, and the high-pass residual module helps by directly injecting spatial detail from panchromatic images. The performance of the proposed network has been compared with that of traditional and other deep-learning-based pansharpening algorithms based on QuickBird, WorldView-3, and Landsat-8 data, and the results demonstrate the superiority of our algorithm.

Automated summary

A novel pansharpening algorithm guided by a deep SR convolutional neural network is proposed in this study, which outperforms traditional and other deep-learning-based methods in improving spatial resolution of images. The algorithm includes components such as SR process, progressive pansharpening process, and high-pass residual module for enhancing spatial details from different types of satellite data.