DNN-based 3D Cloud Retrieval for Variable Solar Illumination and Multiview Spaceborne Imaging (2411.04682v1)

Abstract: 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.

• The paper's main contribution is the PIVOT-CT method, which uses a two-stage DNN training approach to effectively model cloud extinction under variable solar illumination.
• It leverages a new BOMEX dataset with perturbed sun and camera angles, achieving a 15% improvement over previous VIP-CT models in cloud retrieval accuracy.
• The study demonstrates practical implications by enabling scalable, robust 3D cloud tomography for better climate modeling and atmospheric analysis.

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

The paper presents a novel approach to addressing the challenges in 3D cloud tomography using Deep Neural Networks (DNNs). Traditionally, cloud tomography has relied on iterative optimization algorithms that, while accurate, are limited in scalability and speed, particularly when dealing with a high degree of freedom in observational parameters such as solar illumination and camera orientations. The authors propose PIVOT-CT (Projection Integration for Variable Orientation in Computed Tomography), which offers a scalable solution capable of handling variable sun positions and camera arrangements, enhancing cloud reconstruction accuracy by leveraging multiview spaceborne imagery.

Methodological Advances

PIVOT-CT introduces a two-stage training procedure to manage the increased complexity and high variability in solar illumination and camera orientations. The approach builds on prior models, such as VIP-CT, but incorporates dynamic adaptations for solar directions within its DNN architecture. This is achieved by encoding various geometric and atmospheric parameters, including the sun's position, through fully connected layers, enabling the network to predict the 3D distribution of cloud extinction coefficients effectively.

The model leverages a new dataset called BOMEX, which provides labeled cloud field data to train and test these DNNs. The generation of diverse imaging scenarios with varying solar azimuth and zenith angles and perturbed camera poses adds robustness and adaptability to the model. By training on datasets such as BOMEX_sun and BOMEX_perturbed, the model can generalize effectively across different imaging and illumination conditions.

Results

The results section of the paper demonstrates that PIVOT-CT outperforms the existing VIP-CT model by approximately 15% on datasets involving variable sun angles and camera poses. This improvement underscores the method’s robustness to real-world variability in solar illumination, a significant practical advance over previous methods which were restricted to fixed lighting scenarios. Moreover, PIVOT-CT achieves reductions in tomographic error across a range of solar zenith angles, indicating more reliable volumetric cloud retrieval under diverse environmental conditions.

The paper reveals the inherent challenges associated with cloud tomography when the solar angle is near the horizon, resulting in increased attenuation and reduced signal quality. Nonetheless, PIVOT-CT's adaptability across different solar configurations is marked by its ability to incorporate these complexities into a unified retrieval framework, a feat that prior models have struggled to achieve.

Implications and Future Work

The introduction of PIVOT-CT marks a significant stride in the application of DNNs for atmospheric science, specifically in cloud tomography. By integrating varying solar and camera orientations, the method provides a practical foundation for real-time volumetric analysis of clouds—critical for improving climate models and understanding atmospheric dynamics.

Looking forward, further research could explore integrating polarized imaging and other multimodal data to enhance the retrieval of additional atmospheric properties, such as droplet size distribution. Additionally, extending the model to analyze high-dimensional atmospheric phenomena beyond extinction coefficients could open new avenues in the paper of cloud dynamics and their implications for climate science.

Overall, PIVOT-CT provides an advanced computational tool for analyzing cloud structures under varied and realistic remote sensing scenarios, bridging a crucial gap between theoretical model capability and practical application in the field of climate sciences.