Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images

Abstract

This paper presents a novel spatio-contextual fuzzy clustering algorithm for unsupervised change detection from multispectral and multitemporal remote sensing images. The proposed technique uses fuzzy Gibbs Markov Random Field (GMRF) to model the spatial gray level attributes of the multispectral difference image. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the fuzzy GMRF modeled difference image is found to be exponential in nature. Convergence of conventional fuzzy clustering based search criterion is more likely to lead the clustering solutions to be getting trapped in a local minimum. Hence we adhered to the variable neighborhood searching (VNS) based global convergence criterion for iterative estimation of the fuzzy GMRF parameters. Experiments are carried out on different multispectral and multitemporal remote sensing images. Results confirm the effectiveness of the proposed technique. It is also noticed that the proposed scheme provides better results with less misclassification error as compared to the existing techniques. The computational time taken by the proposed technique is comparable with that of the HTNN scheme.