  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-dimensional (1D) convolutional neural networks
(CNN): the LucasCNN, the LucasResNet which contains an identity block as
residual network, and the LucasCoordConv with an additional coordinates layer.
Furthermore, we modify two existing 1D CNN approaches for the presented
classification task. The code of all five CNN approaches is available on GitHub
(Riese, 2019). We evaluate the performance of the CNN approaches and compare
them to a random forest classifier. Thereby, we rely on the freely available
LUCAS topsoil dataset. The CNN approach with the least depth turns out to be
the best performing classifier. The LucasCoordConv achieves the best
performance regarding the average accuracy. In future work, we can further
enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include
additional variables of the rich LUCAS dataset.
