Neural Network Classification of Difficult-to-Distinguish Types of Vegetation on the Basis of Hyperspectral Features

It is experimentally demonstrated that the classification of fragments of a hyperspectral image with preliminary transformation of the spectral features of the image into the principal components and with the use of the Hilbert-Huang spectral transform is fairly effective in the case of vegetation types that are difficult-to-distinguish on the basis of hyperspectra. This classification is compared with traditional methods, where hyperspectral features transformed to the principal components without using spatial information are used. RBF neural networks are used in all methods at the final stage of the classification.