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Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data

Riese, Felix M. 1; Keller, Sina ORCID iD icon 1
1 Karlsruher Institut für Technologie (KIT)


In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework involves two self-organizing maps (SOM) and combines unsupervised with supervised learning. We investigate the impacts of SOM hyperparameters on the regression performance and compare the results of the SOM framework with two established regressors on a measured dataset. The derived results reveal the potential of the SOM framework. Finally, we propose further research aspects for the SOM framework to analyze its capabilities and limitations. We have published our dataset in [1] to ensure the reproducibility of the results.

DOI: 10.1109/IGARSS.2018.8517812
Zitationen: 28
Zitationen: 27
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2153-7003
KITopen-ID: 1000087337
Erschienen in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22nd - 27th July, 2018
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 6151-6154
Schlagwörter Self-organizing feature maps;Hyperspectral imaging;Training;Soil moisture;Soil measurements;Self-organizing maps;machine learning;regression;hyperspectral data;soil moisture
Nachgewiesen in Scopus
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