Contextual high-resolution image classification by Markovian data fusion, adaptive texture extraction, and multiscale segmentation

Spatial-contextual classification methods based either on stochastic Markov random field (MRF) models, on texture analysis, or on region-based processing are important tools for high-resolution multispectral image analysis. In this paper, a novel supervised classification technique is proposed, that integrates the MRF, texture-based, and region-based approaches to contextual image classification in a unique multiscale framework. A previous method, based on the combination of MRFs with multiscale segmentation, is generalized and integrated with the multivariate semivariogram approach to texture analysis. In order to minimize the impact of texture-extraction artifacts at the spatial edges between different classes, an adaptive semivariogram-estimation technique is also developed and iteratively incorporated in the proposed classifier. Experiments are presented with IKONOS images.