A hybrid model integrating spatial pattern, spatial correlation, and edge information for image classification

Abstract

This paper develops a novel hybridmodel that integrates three spatial contexts into probabilistic classifiers for remote sensing classification. First, spatial pattern is introduced using multiple-point geostatistics (MPGs) to characterize the general distribution and arrangement of land covers. Second, spatial correlation is incorporated using spatial covariance to quantify the dependence between pixels. Third, an edge-preserving filter based on the Sobel mask is introduced to avoid the over-smoothing problem. These three types of contexts are combinedwith the spectral information fromthe original image within a higher-orderMarkov random field (MRF) framework for classification. The developed model is capable of classifying complex and diverse land cover types by allowing effective anisotropic filtering of the imagewhile retaining details near edges. Experimentswith three remote sensing images fromdifferent sources based on three probabilistic classifiers obtained results that significantly improved classification accuracies when compared with other popular contextual classifiers and most state-of-the-art methods.