no code implementations • 29 May 2019 • Philipp M. Maier, Sina Keller
This study focuses on the trade-off between the spatial and the spectral resolution of six simulated satellite-based data sets when estimating the chlorophyll a concentration with supervised machine learning models.
no code implementations • 3 Apr 2019 • Philipp M. Maier, Sina Keller
In further studies, we will focus on the application of machine learning models on spectral satellite data to enhance the area-wide estimation of chlorophyll a concentration for inland waters.
1 code implementation • 26 Mar 2019 • Felix M. Riese, Sina Keller
In this paper, we introduce the freely available Supervised Self-organizing maps (SuSi) Python package which performs supervised regression and classification.
1 code implementation • 15 Jan 2019 • Felix M. Riese, Sina Keller
In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.
no code implementations • 3 May 2018 • Philipp M. Maier, Sina Keller
Then, we evaluate the performance of the regression framework with and without this preprocessing step.
1 code implementation • 24 Apr 2018 • Sina Keller, Felix M. Riese, Johanna Stötzer, Philipp M. Maier, Stefan Hinz
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data.
1 code implementation • 14 Apr 2018 • Felix M. Riese, Sina Keller
In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model.