Neural network classification of soils with different carbon and calcium content based on hyperspectral data

Abstract

The paper proposes approaches for the classification of high-resolution hyperspectral images in the problem of classification of soil species classification. A spectral-spatial convolutional neural network with compensation for illumination variations is used as a classifier. The effectiveness of the proposed approach in the problem of classification of hyperspectral images of soils obtained by a scanning type hyperspectrometer is shown. A multiclass neural network is compared with an ensemble in which the results of a multiclass neural network are refined by several binary classifiers. It is shown that the use of normalization of illumination inhomogeneity and the use of an ensemble of convolutional spatial-spectral neural networks can significantly increase the accuracy of soil type classification.