most citedSoil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data

53 citations · 66 across the 2 of their papers we have counts for

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cs.CV201913 cited

Estimating Chlorophyll a Concentrations of Several Inland Waters with Hyperspectral Data and Machine Learning Models

Philipp M. Maier, Sina Keller

Water is a key component of life, the natural environment and human health. For monitoring the conditions of a water body, the chlorophyll a concentration can serve as a proxy for…

cs.CV201953 cited

Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data

Felix M. Riese, Sina Keller

Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1…

cs.CV2018

Machine learning regression on hyperspectral data to estimate multiple water parameters

Philipp M. Maier, Sina Keller

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-senso…

cs.CV2018

Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

Sina Keller, Felix M. Riese, Johanna Stötzer +2

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field…

cs.CV2018

Fusion of hyperspectral and ground penetrating radar to estimate soil moisture

Felix M. Riese, Sina Keller

In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data…