A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential...

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Bibliographic Details
Main Authors: Katja Berger, Juan Pablo Rivera Caicedo, Luca Martino, Matthias Wocher, Tobias Hank, Jochem Verrelst
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Remote Sensing
Subjects:
Gaussian process regression
EnMAP
hyperspectral
query strategies
optimal experimental design
Online Access:https://www.mdpi.com/2072-4292/13/2/287

Internet

https://www.mdpi.com/2072-4292/13/2/287

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