Multilayered autoencoders in problems of hyperspectral image analysis and processing
A model of five-layered autoencoder (stacked autoencoder, SAE) is suggested for deep image features extraction and deriving compressed hyperspectral data set specifying the image. Spectral cost function, dependent on spectral curve forms of hyperspectral image, has been used for the autoencoder tuning. At the first step the autoencoder capabilities will be tested based on using pure spectral information contained in image data. The images from well known and widely used hyperspectral databases (Indian Pines, Pavia University è KSC) are planned to be used for the model testing.
deep neural networks, multilayered autoencoders (stacked autoencoders, SAE), hóperspectral images, image feature extraction, hóperspectral data compression
Publication language:russian, pages:21
Mathematical modelling in actual problems of science and technics