| 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 | 22 | 2018 |
| 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 | 16 | 2020 |
| 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 | 15 | 2018 |
| 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 | 10 | 2018 |
| 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 | 5 | 2019 |
| 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 | 5 | 2018 |
| 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 | 5 | 2018 |
| Supervised, Semi-Supervised, and Unsupervised Learning for Hyperspectral Regression FM Riese, S Keller Hyperspectral Image Analysis, 187-232, 2020 | 4 | 2020 |
| Examples for CNN training and classification on Sentinel-2 data J Leitloff, FM Riese https://doi.org/10.5281/zenodo.3268451, 2018 | 4 | 2018 |
| Susi: Supervised self-organizing maps for regression and classification in python FM Riese, S Keller arXiv preprint arXiv:1903.11114, 2019 | 3 | 2019 |
| SuSi: Supervised Self-organizing Maps in Python FM Riese, S Keller https://doi.org/10.5281/zenodo.2609130, 2019 | 3 | 2019 |
| Code for Deep Learning for Land Cover Change Detection O Sefrin, FM Riese, S Keller https://doi.org/10.5281/zenodo.4289079, 2020 | 2 | 2020 |
| Development and Applications of Machine Learning Methods for Hyperspectral Data FM Riese https://doi.org/10.5445/IR/1000120067, 2020 | 2 | 2020 |
| 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 | 2 | 2019 |
| Hyperspectral benchmark dataset on soil moisture FM Riese, S Keller https://doi.org/10.5281/zenodo.1227837, 2018 | 2 | 2018 |
| Deep Learning for Land Cover Change Detection O Sefrin, FM Riese, S Keller Remote Sensing 13 (1), 78, 2021 | | 2021 |
| 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 |
Philipp MaierKarlsruhe Institute of TechnologyVerified email at kit.edu
