Multi-spectral texture segmentation based on the spectral cooccurrence matrix

Multi-spectral images are becoming more common in industrial inspection ia ks where the colour is used as a quality measure. In this paper we propose a spectral cooccurrence matrix-based method tu analyse multi-spectral textu re images, in which every pixel contains a measured colour spectrum. We fir st quantise the spectral domain of the multi-spectral images using the Self -Organising Mao (SOM). Next we label the spectral domain according to the q uantised spectra. In the spatial domain, we represent a multi-spectral text ure using thf spectral cooccurrence matrix, which we calculate from the lab elled image. In the experimental part of this paper, we present the results of segmenting natural multi-spectral textures. We compared. the k-nearest neighbour (k-NN) classifier and the multilayer perceptron (MLP) neural netw ork-based segmentation results of the multi-spectral and RGB colour texture s.