New Statistical Methods for Computer-Assisted Inversion with Applications to Satellite Remote Sensing

将卫星遥感数据转化为有用的气候和地球物理信息需要求解辐射传输方程。由于需要处理整个地球规模的数据，目前用于近似解决这些复杂方程的方法是有限的，并且没有系统地利用多传感器测量、地面测量和起作用的时间动力学。与此同时，世界各国越来越多地利用遥感数据来应对气候变化和环境退化的挑战。为了准确地告知利益相关者，需要更好的统计模型，特别是对于地面测量有限的发展中国家地区的成像。这项研究的目标是开发新一代统计方法，在更局部的层面上求解卫星遥感数据处理的核心方程。将卫星遥感数据转化为有用的气候和地球物理信息需要解决一些非常重要的辐射传输逆问题。该项目旨在开发一个贝叶斯框架，将展开深度学习模型的算法和前向计算机代码结合到反演图中。将开发迁移学习和强化学习框架，将计算机中学习的反演图与地面测量相结合，调整分布失配并随着时间的推移保持反演过程的准确性，即使卫星数据分布随时间变化也是如此。该项目还将在理论层面上有助于从统计学上更深入地理解强化学习和算法展开模型。该项目旨在改进全球遥感数据的分析，应用于气候变化，连接遥感、机器学习和统计学等学科。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The transformation of satellite-based remotely sensed data into useful climate and geophysical information requires solving radiative transfer equations. Due to the need to process data at the scale of the entire planet, the methodologies currently used to approximate solutions of these complicated equations are limited and do not systematically exploit multi-sensor measurements, ground measurements, and the temporal dynamics at play. At the same time, countries around the world are increasingly turning to remote sensing data to cope with the challenges related to climate change and environmental degradation. To accurately inform stakeholders, better statistical models are needed, particularly for imaging of regions in the developing world where ground measurements are limited. The goal of this research is to develop a new generation of statistical methods for solving, at a more local level, the equations at the heart of satellite remote sensing data processing.The transformation of satellite-based remotely sensed data into useful climate and geophysical information requires solving some highly non-trivial radiative transfer inverse problems. This project aims to develop a Bayesian framework that combines algorithm unrolling deep learning models and a forward computer code into an inversion map. A transfer learning and a reinforcement learning framework will be developed to combine the inversion map learned in-silico with ground measurements, to adjust for distributional mismatch and to maintain the accuracy of the inversion procedure over time, even as the satellite data distribution changes over time. The project will also contribute at the theoretical level to a statistically deeper understanding of reinforcement learning and algorithm unrolling models. The project aims to improve analysis of global remote sensing data with applications to climate change, bridging of the disciplines of remote sensing, machine learning, and statistics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.