Open AccessNeural network texture segmentation of satellite images of woodlands using the U-net model
Author(s) -
A.E. Alyokhina,
D.S. Rusin,
Е. В. Дмитриев,
Anastasiia Safonova
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.25743/sdm.2021.70.49.004
Subject(s) - panchromatic film , artificial intelligence , computer science , multispectral image , pattern recognition (psychology) , wavelet , segmentation , image texture , wavelet transform , convolutional neural network , feature extraction , artificial neural network , image segmentation , computer vision
With the advent of space equipment that allows obtaining panchromatic images of ultra-high spatial resolution (< 1 m) there was a tendency to develop methods of thematic processing of aerospace images in the direction of joint use of textural and spectral features of the objects under study. In this paper, we consider the problem of classification of forest canopy structures based on textural analysis of multispectral and panchromatic images of Worldview-2. Traditionally, a statistical approach is used to solve this problem, based on the construction of distributions of the common occurrence of gray gradations and the calculation of statistical moments that have significant regression relationships with the structural parameters of stands. An alternative approach to solving the problem of extracting texture features is based on frequency analysis of images. To date, one of the most promising methods of this kind is based on wavelet scattering. In comparison with the traditionally applied approaches based on the Fourier transform, in addition to the characteristic signal frequencies, the wavelet analysis allows us to identify characteristic spatial scales, which is fundamentally important for the textural analysis of spatially inhomogeneous images. This paper uses a more general approach to solving the problem of texture segmentation using the convolutional neural network U-net. This architecture is a sequence of convolution-pooling layers. At the first stage, the sampling of the original image is lowered and the content is captured. At the second stage, the exact localization of the recognized classes is carried out, while the discretization is increased to the original one. The RMSProp optimizer was used to train the network. At the preprocessing stage, the contrast of fragments is increased using the global contrast normalization algorithm. Numerical experiments using expert information have shown that the proposed method allows segmenting the structural classes of the forest canopy with high accuracy.