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

计算机辅助反演统计新方法及其在卫星遥感中的应用

基本信息

批准号：
2210664
负责人：
Yves Atchade
金额：
$ 36.53万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210664&HistoricalAwards=false
关键词：
New Statistical Methods Computer Assisted

项目摘要

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.

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。由于需要在整个地球的尺度上处理数据，目前用于近似求解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，地面测量和时间动态。与此同时，世界各国越来越多地转向遥感数据，以科普与气候变化和环境退化有关的挑战。为了准确地向利益攸关方提供信息，需要更好的统计模型，特别是对地面测量有限的发展中国家区域进行成像。这项研究的目标是开发新一代的统计方法，解决，在更本地的水平上，在卫星遥感数据处理的核心方程，基于卫星的遥感数据转换成有用的气候和地球物理信息需要解决一些高度非平凡的辐射传输逆问题。该项目旨在开发一个贝叶斯框架，将算法展开深度学习模型和前向计算机代码结合到反演映射中。将开发一个转移学习和强化学习框架，以便将计算机模拟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程序的准确性，即使卫星数据分布随着时间的推移而变化。该项目还将在理论层面上对强化学习和算法展开模型的统计学更深入的理解做出贡献。该项目旨在改善全球遥感数据的分析与气候变化的应用，桥接遥感，机器学习和统计学科。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。