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.
Abstract Improvements in optical remote sensing spectral resolution and increased data volumes necessitates the development of improved techniques for quantitative geological analysis. Laboratory spectral studies indicate that absorption band positions, depths and widths are correlated with diagnostic physicochemical mineral properties such as composition and abundance. Most current analytical techniques are incapable of providing comprehensive quantitative analysis of hyperspectral geological remote sensing data. Factors which must be considered for hyperspectral remote sensing campaigns include spectral resolution, analytical technique, band pass positions and spatial resolution. In many cases the volume of data required to address specific issues can be reduced through intelligent selection of band passes and analytical techniques.