Multispectral Remote Sensing Image Segmentation Using Gaussian Copula

To take full advantage of inter-band correlations of multispectral remote sensing images, a multispectral remote sensing image segmentation method based on Gaussian copula function is proposed. Firstly, the Markov random field model is exploited to establish a label field and the label field is characterized by the Potts model. Then, the feature field characterizing pixel spectral measurements is built. A multivariate statistical model based on Gaussian copula modeling pixel spectral measurement is proposed. Furthermore, a posterior probability model of multispectral remote sensing image segmentation is established by Bayes theorem combined with the label field model, the feature field model and the prior probabilities of model parameters. The Metropolis-Hastings algorithm is designed to simulate the posterior probability model, and the optimal segmentation is obtained under the maximum a posterior strategy. Experiments are carried out with simulated and real multispectral images respectively, and experimental results indicate that the proposed algorithm has a strong ability to describe the correlation between bands with a high accuracy. © 2019, Science Press. All right reserved.