Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval

• URL: http://arxiv.org/abs/2012.04468v1
• Date: Mon, 7 Dec 2020 08:56:40 GMT
• Title: Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
• Authors: ochem Verrelst, Sara Dethier, Juan Pablo Rivera, Jordi Mu\~noz-Mar\'i, Gustau Camps-Valls, Jos\'e Moreno
• Abstract summary: Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for operational biophysical variable retrieval schemes. They face difficulties in coping with large training datasets. Active learning (AL) methods enable to select the most informative samples in a dataset. This letter introduces six AL methods for achieving optimized biophysical variable estimation with a manageable training dataset.
• Score: 6.093845877765489
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 datasets. 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 dataset. This letter introduces six AL methods for achieving optimized biophysical variable estimation with a manageable training dataset, and their implementation into a Matlab-based MLRA toolbox for semi-automatic use. The AL methods were analyzed on their efficiency of improving the estimation accuracy of 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 development of operational retrieval models.

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