论文标题
用高斯过程中的地球观察数据中的学习结构
Learning Structures in Earth Observation Data with Gaussian Processes
论文作者
论文摘要
高斯工艺（GPS）在过去几年中总体而言，在地球科学和生物地球物理参数检索方面取得了巨大的成功。 GP构成一个坚实的贝叶斯框架，以始终如一地制定许多功能近似问题。本文回顾了该领域的主要理论GP发展。我们回顾了尊重信号和噪声特征的新算法，这些算法自动提供了特征排名，并允许相关的不确定性间隔适用于时空中的GP模型。通过一组说明性示例，在地球科学和遥感领域的地球科学和遥感领域进行了说明。
Gaussian Processes (GPs) has experienced tremendous success in geoscience in general and for bio-geophysical parameter retrieval in the last years. GPs constitute a solid Bayesian framework to formulate many function approximation problems consistently. This paper reviews the main theoretical GP developments in the field. We review new algorithms that respect the signal and noise characteristics, that provide feature rankings automatically, and that allow applicability of associated uncertainty intervals to transport GP models in space and time. All these developments are illustrated in the field of geoscience and remote sensing at a local and global scales through a set of illustrative examples.
