首个可处理多视角与变光照的三维云场深度学习反演系统
DNN-based 3D Cloud Retrieval for Variable Solar Illumination and Multiview Spaceborne Imaging
- 采用两阶段训练的DNN，融合多视角图像与光照姿态信息
- 在太阳天顶角变化时性能显著优于现有方法
- 适合需要全球大尺度三维云数据的气候研究者
气候研究常依赖遥感图像获取二维云特性图。为推动体积分析，本文聚焦于利用多视角遥感数据恢复浅层云的三维非均匀消光系数场。气候研究需全球大规模统计数据，以往深度神经网络（DNN）可在星载遥感下行速率下实现推理。但先前方法仅适用于固定太阳光照方向。本文提出首个可扩展的DNN系统，支持变化的相机位姿与太阳方向。通过整合多视角云强度图像、相机姿态及太阳方向数据，显著提升恢复灵活性。采用新颖的两阶段训练方案以应对该问题中高自由度挑战。实验表明，本方法在太阳天顶角变化场景下相较当前最优方法有显著提升。
原文摘要 · Abstract (English)
Climate studies often rely on remotely sensed images to retrieve two-dimensional maps of cloud properties. To advance volumetric analysis, we focus on recovering the three-dimensional (3D) 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 3D cloud retrieval that accommodates varying camera poses and solar directions. By integrating multiview cloud intensity images with camera poses 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, particularly in handling variations in the sun's zenith angle.
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