Superresolution Imaging With a Deep Multipath Network for the Reconstruction of Satellite Cloud Images

Abstract

Abstract 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.

Keywords

• contrails
• deep learning
• Himawari‐8 satellite
• satellite cloud image
• superresolution