Hyperspectral Imaging for Soil Type Classification

This paper explores the application of soil type classification algorithms (red clay, loamy soil, chernozem) in agricultural fields based on hyperspectral imaging data (400–1000 nm) acquired from an unmanned aerial vehicle (UAV). As part of the dataset preparation, the data were processed using a set of algorithms, from classical approaches to deep models. The ensemble NM3D-CNN achieved the best performance—0.874 weighted F1. Results of other approaches—PCA segmentation: 0.864, 1D-CNN: 0.842, SSFTT: 0.829, SVM: 0.764. These results indicate that Vis-NIR UAV-borne HIS enables reliable soil mapping, while spectral-spatial deep ensembles provide the strongest gains.