Texture classification in aerial photographs using multiscale and multilayer complex networks

Abstract

Texture classification using oriented complex networks considers the functional connections between topological elements and simulates the complex textures more accurately. In contrast to the classical spatial texture analysis, we offer a novel function of weights in complex networks and a classification method that takes into account the scaling and color of textures. For this, three complex networks represented R, G and B components are built, which provide invariance of color aerial photographs obtained at different times. Comparison of the classification results using the proposed multiscale complex networks and conventional texture analysis based on a statistical approach is given. Also we extended this approach on color aerial photographs using multilayer structure of complex network.