Deep Markov models of multidimensional random fields

Abstract

Abstract The paper discusses the use of a doubly stochastic random field model based on autoregressions with multiple roots of characteristic equations for solving the problem of image generation and segmentation. It was proposed to form an image taking into account hidden Markov parameter fields. In particular, the correlation coefficients of the model, as well as the average values and brightness variances, are realizations of random fields, the assessment of which in explicit form is not possible. In addition, a modification of a variational autoencoder for a doubly stochastic model is considered. Finally, based on the use of a modified variational auto encoder, classification and segmentation of satellite images is performed. The combined use of the developed segmentation algorithm with the Unet neural network provides an increase in segmentation accuracy of 3%.