Parameter optimization for Markov random field models for remote sensing image classification through sequential minimal optimization

This paper addresses the problem of parameter optimization for Markov random field (MRF) models for supervised classification of remote sensing images. MRF model parameters generally impact on classification accuracy, and their automatic optimization is still an open issue especially in the supervised case. The proposed approach combines a mean square error (MSE) formulation with Platt's sequential minimal optimization algorithm, with the aim of taking benefit from the effectiveness of this quadratic programming technique in both computation time and memory occupation. The experimental validation is carried out with five real data sets comprising multipolarization and multifrequency SAR, multispectral high-resolution, single date and multitemporal imagery. The method is compared with two techniques based on MSE criteria and on the Ho-Kashyap and Goldfard-Idnani numerical algorithms.