An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery

Texture analysis of remote sensing images based on classification of area units represented in image segments is usually more accurate than operating on an individual pixel basis. In this paper we suggest a two-step procedure to segment texture patterns in remotely sensed data. An image is first classified based on texture analysis using a multi-parameter and multi-scale technique. The intermediate results are then treated as initial segments for subsequent segmentation based on the Gaussian Markov random field (GMRF) model. The segmentation procedure seeks to merge pairs of segments with the minimum variance difference. Experiments using real data prove that the two-step procedure improves both computational efficiency and accuracy of texture classification.