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Found 7 papers, 4 papers with code
Application of Different Simulated Spectral Data and Machine Learning to Estimate the Chlorophyll a Concentration of Several Inland Waters
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.
Estimating Chlorophyll a Concentrations of Several Inland Waters with Hyperspectral Data and Machine Learning Models
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.
SuSi: Supervised Self-Organizing Maps for Regression and Classification in Python
In this paper, we introduce the freely available Supervised Self-organizing maps (SuSi) Python package which performs supervised regression and classification.
Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data
In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.
Machine learning regression on hyperspectral data to estimate multiple water parameters
Then, we evaluate the performance of the regression framework with and without this preprocessing step.
Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data.
Fusion of hyperspectral and ground penetrating radar to estimate soil moisture
In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model.
