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Felix M. Riese
Felix M. Riese
Researcher, Karlsruhe Institute of Technology
Bestätigte E-Mail-Adresse bei kit.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Deep learning for land cover change detection
O Sefrin, FM Riese, S Keller
Remote Sensing 13 (1), 78, 2020
1122020
Supervised and semi-supervised self-organizing maps for regression and classification focusing on hyperspectral data
FM Riese, S Keller, S Hinz
Remote Sensing 12 (1), 7, 2019
1082019
Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity
S Keller, PM Maier, FM Riese, S Norra, A Holbach, N Börsig, A Wilhelms, ...
International journal of environmental research and public health 15 (9), 1881, 2018
962018
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
FM Riese, S Keller
ISPRS Ann. Photogramm. Remote Sens. Spat., 615-621, 2019
752019
Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data
FM Riese, S Keller
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing …, 2018
372018
Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
FM Torun, S Virreira Winter, S Doll, FM Riese, A Vorobyev, ...
Journal of Proteome Research 22 (2), 359-367, 2022
302022
Supervised, semi-supervised, and unsupervised learning for hyperspectral regression
FM Riese, S Keller
Hyperspectral image analysis: Advances in machine learning and signal …, 2020
252020
Developing a Machine Learning Framework for Estimating Soil Moisture with VNIR hyperspectral data
S Keller, FM Riese, J Stötzer, PM Maier, S Hinz
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information …, 2018
222018
Hyperspectral benchmark dataset on soil moisture
FM Riese, S Keller
https://doi.org/10.5281/zenodo.1227837, 2018
132018
Susi: Supervised self-organizing maps for regression and classification in python
FM Riese, S Keller
arXiv preprint arXiv:1903.11114, 2019
122019
Fusion of hyper spectral and ground penetrating radar data to estimate soil moisture
FM Riese, S Keller
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in …, 2018
112018
Examples for CNN training and classification on Sentinel-2 data
J Leitloff, FM Riese
https://doi.org/10.5281/zenodo.3268451, 2018
92018
SuSi: Supervised Self-organizing Maps in Python
FM Riese, S Keller
https://doi.org/10.5281/zenodo.2609130, 2019
72019
Modeling Subsurface Soil Moisture Based on Hyperspectral Data: First Results of a Multilateral Field Campaign
S Keller, FM Riese, N Allroggen, C Jackisch, S Hinz
Tagungsband der 37. Wissenschaftlich-Technische Jahrestagung der DGPF e.V …, 2018
62018
Development and Applications of Machine Learning Methods for Hyperspectral Data
FM Riese
https://doi.org/10.5445/IR/1000120067, 2020
52020
Code for Deep Learning for Land Cover Change Detection
O Sefrin, FM Riese, S Keller
https://doi.org/10.5281/zenodo.4289079, 2020
22020
Hyperspectral Processing Scripts for the HydReSGeo Dataset
FM Riese
https://doi.org/10.5281/zenodo.3706418, 2020
22020
Solutions and planning tools for water supply and wastewater management in prosperous regions tackling water scarcity
CD León, H Kosow, Y Zahumensky, M Krauß, S Wasielewski, R Minke, ...
Mid-Term Conference–Frankfurt am Main, Germany 20-21 February 2019, 28, 2019
22019
Boosted-jet reconstruction methods in a search for Higgs-boson production in association with a top-quark-antiquark pair at the CMS experiment
F Riese
12017
Using patient-reported quality of life outcomes to assess the validity of smartphone-based remote assessment of motor and cognitive performance in manifest Huntington’s disease …
F Riese, J Rennig, F Lipsmeier, A Bamdadian, S Schobel, C Gossens, ...
MDS Virtual Congress 2021 36 (suppl 1), 2021
2021
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