Felix M. Riese
Title
Cited by
Cited by
Year
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
192018
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
132018
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, 2020
92020
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
82018
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
52018
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
52018
SuSi: Supervised Self-organizing Maps in Python
FM Riese, S Keller
https://doi.org/10.5281/zenodo.2609130, 2019
32019
Susi: Supervised self-organizing maps for regression and classification in python
FM Riese, S Keller
arXiv preprint arXiv:1903.11114, 2019
22019
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
22019
Hyperspectral benchmark dataset on soil moisture
FM Riese, S Keller
https://doi.org/10.5281/zenodo.1227837, 2018
22018
Examples for CNN training and classification on Sentinel-2 data
J Leitloff, FM Riese
https://doi.org/10.5281/zenodo.3268451, 2018
22018
Supervised, Semi-supervised, and Unsupervised Learning for Hyperspectral Regression
FM Riese, S Keller
Hyperspectral Image Analysis, 187-232, 2020
12020
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
12019
Development and Applications of Machine Learning Methods for Hyperspectral Data
FM Riese
https://doi.org/10.5445/IR/1000120067, 2020
2020
HydReSGeo: Field experiment dataset of surface-sub-surface infiltration dynamics acquired by hydrological, remote sensing, and geophysical measurement techniques
S Keller, FM Riese, N Allroggen, C Jackisch
https://doi.org/10.5880/fidgeo.2020.015, 2020
2020
Aerial Peruvian Andes Campaign (ALPACA) Dataset 2019
FM Riese, S Schroers, J Wienhöfer, S Keller
https://doi.org/10.5445/IR/1000118082, 2020
2020
LUCAS Soil Texture Processing Scripts
FM Riese
https://doi.org/10.5281/zenodo.3871431, 2020
2020
Processing Scripts for the ALPACA Dataset
FM Riese
https://doi.org/10.5281/zenodo.3871459, 2020
2020
Hyperspectral Processing Scripts for the HydReSGeo Dataset
FM Riese
https://doi.org/10.5281/zenodo.3706418, 2020
2020
Fernerkundung mit multispektralen Satellitenbildern
J Leitloff, FM Riese, N Kreiling
2019
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Articles 1–20