DNN-Based 3-D Cloud Retrieval for Variable Solar Illumination and Multiview Spaceborne Imaging

Abstract

Climate studies often rely on remotely sensed images to retrieve 2-D maps of cloud properties. To advance volumetric analysis, we focus on recovering the 3-D heterogeneous extinction coefficient field of shallow clouds using multiview remote sensing data. Climate research requires large-scale worldwide statistics. To enable scalable data processing, previous deep neural networks (DNNs) can infer at spaceborne remote sensing downlink rates. However, prior methods are limited to a fixed solar illumination direction. In this work, we introduce the first scalable DNN-based system for 3-D cloud retrieval that accommodates varying camera positions and solar directions. By integrating multiview cloud intensity images with camera position and solar direction data, we achieve greater flexibility in recovery. Training of the DNN is performed by a novel two-stage scheme to address the high number of degrees of freedom in this problem. Our approach shows substantial improvements over previous state-of-the-art methods, particularly in handling variations in the sun’s zenith angle.