Neural network analysis of hyperspectral images of soil

The article approaches to the classification of high-resolution hyperspectral images in the problem of classification of soil species is proposed. A spectral-spatial convolutional neural network with compensation for lighting 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 hyperspectral camera is shown. The essence of the developed method is to use binary classification together with multiclass, thereby improving the result of the latter

Hyperspectral images neural network analysis of unstained micropreparations

The article presents the results of a study of hyperspectral imaging in microscopy to assess pathological changes in unstained medical micropreparations.Hyperspectral imaging was carried out using a system of synchronous shooting and movement of a movable table combined with a stepper motor. To improve the quality of theobtained images, software correction of the illumination of the spectral channels was used. The classification was carried out by a convolutional neural network. This method may be promising for assessing pathological changes in clinical practice. Experimental studies were carried out on histological preparations with different types of tissues without staining with contrasting medical dyes. To assess the reliability of the classification method, a comparison was made with thestandard method using staining of the studied samples