Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 13, Issue 7, Pages 1012-1016Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2016.2560799
Keywords
Active learning (AL); hybrid retrieval methods; kernel methods; machine learning regression algorithms (MLRAs); PROSAIL; Sentinel-3
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Funding
- Spanish Ministry of Economy and Competitiveness [TIN2015-64210-R]
- European Space Agency project FLEX/S3 Tandem Mission Performance Analysis and Requirements Consolidation Study [RFQ 3-13397/11/NL/CBi]
- European Space Agency project FLEX-Bridge Study [RFP IPL-PEO/FF/lf/14.687]
- European Research Council (ERC) under the ERC-CoG Project [647423]
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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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