Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval

Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing da...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 13; no. 7; pp. 1012 - 1016
Main Authors: Verrelst, Jochem, Dethier, Sara, Rivera, Juan Pablo, Munoz-Mari, Jordi, Camps-Valls, Gustau, Moreno, Jose
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Active learning (AL)
Biological system modeling
Chlorophylls
Computational modeling
Computer simulation
Data models
Data reduction
Estimation
Feature extraction
hybrid retrieval methods
Kernel
kernel methods
Learning
machine learning regression algorithms (MLRAs)
Mathematical models
PROSAIL
Radiative transfer
Remote sensing
Retrieval
Sentinel-3
Training
Training data
kernel methods
PROSAIL
hybrid retrieval methods
machine learning regression algorithms (MLRAs)
Sentinel-3
Active learning (AL)
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimation with a manageable training data set, and their implementation into a Matlab-based MLRA toolbox for semiautomatic use. The AL methods were analyzed on their efficiency of improving the estimation accuracy of the leaf area index and chlorophyll content based on PROSAIL simulations. Each of the implemented methods outperformed random sampling, improving retrieval accuracy with lower sampling rates. Practically, AL methods open opportunities to feed advanced MLRAs with RTM-generated training data for the development of operational retrieval models.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2016.2560799