Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features

Abstract

Gaussian Markov random fields (GMRFs) are used to analyze textures. GMRFs measure the interdependence of neighboring pixels within a texture to produce features. In this paper, neighboring pixels are taken into account in a priority sequence according to their distance from the center pixel, and a step-by-step least squares method is proposed to extract a novel set of GMRF texture features, named as PS-GMRF. A complete procedure is first designed to classify texture samples of QuickBird imagery. After texture feature extraction, a subset of PS-GMRF features is obtained by the sequential floating forward-selection method. Then, the maximum a posteriori iterated conditional mode classification algorithm is used, involving the selected PS-GMRF texture features in combination with spectral features. The experimental results show that the performance of classifying texture samples on high spatial resolution QuickBird satellite imagery is improved when texture features and spectral features are used jointly, and PS-GMRF features have a higher discrimination power compared to the classical GMRF features, making a notable improvement in classification accuracy from 71.84% to 94.01%. On the other hand, it is found that one of the PS-GMRF texture features - the lowest order variance - is effective for residential-area detection. Some results for IKONOS and SPOT-5 images show that the integration of the lowest order variance with spectral features improves the classification accuracy compared to classification with purely spectral features