Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference

TL;DR

This paper presents warped Gaussian processes (WGP) for improved remote sensing parameter estimation and causal inference, demonstrating superior accuracy and confidence intervals over standard models in various geoscience applications.

Contribution

Introduction of warped Gaussian processes for remote sensing, enabling nonlinear output transformations and improved estimation and causal inference performance.

Findings

WGP outperforms standard GP in chlorophyll and vegetation parameter estimation.

WGP provides more accurate confidence intervals.

WGP effectively detects causal directions in geoscience data.

Abstract

This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are then learned via standard maximum likelihood. We show the good performance of the proposed model for the estimation of oceanic chlorophyll content from multispectral data, vegetation parameters (chlorophyll, leaf area index, and fractional vegetation cover) from hyperspectral data, and in the detection of the causal direction in a collection of 28 bivariate geoscience and remote sensing causal problems. The model consistently performs better than the standard GP and the more advanced heteroscedastic GP model, both in terms of accuracy and more sensible confidence intervals.