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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 13; H. 7; S. 1012 - 1016
Hauptverfasser:	Verrelst, Jochem, Dethier, Sara, Rivera, Juan Pablo, Munoz-Mari, Jordi, Camps-Valls, Gustau, Moreno, Jose
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!