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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Vydáno v:	IEEE geoscience and remote sensing letters Ročník 13; číslo 7; s. 1012 - 1016
Hlavní autoři:	Verrelst, Jochem, Dethier, Sara, Rivera, Juan Pablo, Munoz-Mari, Jordi, Camps-Valls, Gustau, Moreno, Jose
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.07.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
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