A LABELING SCHEME BASED ON MARKOV RANDOM FIELDS AND GAUSSIAN MIXTURE MODELS FOR HYPERSPECTRAL IMAGES

摘要：

A new method about surface feature labeling for hyperspectral images is presented in this paper in the framework of Baycsian labeling based on Markov Random Field (MRF). After the dimension of the hyperspectral image is reduced by PCA, a kernel density estimator and a Gaussian mixture model (GMM) are respectively used to capture the non-Gaussian statistics of the dimension-reduced images and their difference images. Further more, one of components of GMM is chosen to describe the energy of difference images to improve classification accuracy. A Markov random field-maximum a posteriori estimation problem is formulated and the final labels are obtained by the simulated annealing algorithm. Additionally, the labeling result based on GMM is compared with Generalized Laplacian (GL) model. Experimental results show that it is an efficient and robust algorithm for surface feature labeling.

关键词： Hyperspectral image Markov random field (MRF) Non-Gaussian Statistics Gaussian Mizture Model (GMM) Nonparametric Kernel Density Estimation Labeling

作者: XIU-QIN HUANG ZHI-WU LIAO

作者单位: Suzhou Non-ferrous Metals Research Institute.Suzhou, Jiangsu, China School of Computer Science, Sichuan Normal University, Chengdu, Sichuan, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3619-3624

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）