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

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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.