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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| Vydané v: | IEEE geoscience and remote sensing letters Ročník 13; číslo 7; s. 1012 - 1016 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1545-598X, 1558-0571 |
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| 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 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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| AbstractList | 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. |
| Author | Verrelst, Jochem Munoz-Mari, Jordi Rivera, Juan Pablo Dethier, Sara Moreno, Jose Camps-Valls, Gustau |
| Author_xml | – sequence: 1 givenname: Jochem surname: Verrelst fullname: Verrelst, Jochem email: jochem.verrelst@uv.es organization: Image Process. Lab., Univ. de Valencia, València, Spain – sequence: 2 givenname: Sara surname: Dethier fullname: Dethier, Sara email: sara.dethier11@imperial.ac.uk organization: Dept. of Phys., Imperial Coll. London, London, UK – sequence: 3 givenname: Juan Pablo surname: Rivera fullname: Rivera, Juan Pablo email: juan.rivera@uv.es organization: Image Process. Lab., Univ. de Valencia, València, Spain – sequence: 4 givenname: Jordi surname: Munoz-Mari fullname: Munoz-Mari, Jordi email: jordi.munoz@uv.es organization: Image Process. Lab., Univ. de Valencia, València, Spain – sequence: 5 givenname: Gustau surname: Camps-Valls fullname: Camps-Valls, Gustau email: gustau.camps@uv.es organization: Image Process. Lab., Univ. de Valencia, València, Spain – sequence: 6 givenname: Jose surname: Moreno fullname: Moreno, Jose email: jose.moreno@uv.es organization: Image Process. Lab., Univ. de Valencia, València, Spain |
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| SubjectTerms | 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 |
| Title | Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval |
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