Open AccessSuperresolution Imaging With a Deep Multipath Network for the Reconstruction of Satellite Cloud Images
Author(s) -
Zhang Jinglin,
Yang Zhipeng,
Jia Zhaoying,
Bai Cong
Publication year - 2021
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2020ea001559
Subject(s) - computer science , satellite , residual , cloud computing , typhoon , remote sensing , data set , artificial intelligence , projection (relational algebra) , multipath propagation , computer vision , meteorology , geology , geography , telecommunications , algorithm , engineering , operating system , channel (broadcasting) , aerospace engineering
Satellite cloud images play an important role in weather analysis and forecast. High‐resolution satellite images play a significant role in the study of mesoscale weather systems such as typhoons. With the increasing demands of locating and tracking techniques, the resolution of satellite images is no longer satisfactory. Enhancing their resolution with superresolution (SR) methods can help in identifying and locating weather systems. In this paper, we propose a multipath network model, called SRCloudNet, that involves joint training of a back‐projection network and a local residual network. SRCloudNet integrates features extracted from back‐projection units and residual dense blocks to achieve more accurate image reconstruction. We also developed a novel natural‐color cloud and contrail image data set, constituting the first‐ever satellite cloud image data set established for SR research. Because of the special features, contrail images were first used to test the performance of SRCloudNet. Extensive experiments demonstrated that SRCloudNet achieves superior performance.