GAUSSIAN MIXTURE MODELS CLUSTERING USING MARKOV RANDOM FIELD FOR MULTISPECTRAL REMOTE SENSING IMAGES

摘要：

Multispectral images provide detailed data with information in both the spatial and spectral domains. Many clustering methods for multispectral images are based on a per-pixel classification, while uses only spectral information and ignores spatial information. In this work, a new clustering algorithm for multispectral images, based on both spectral and spatial information, is presented. This algorithm is integrated with generalized mixture Gaussian model (GMM) and Markov random field (MRF). The number of clusters is automatically identified by using the pseudolikelihood information criterion (PLIC). We examine the behavior of this model when applied to multispectral remote sensing images.

关键词： Image clustering Gaussian mixture model Markov random field (MRF) expectation maximization (EM) spatial information

作者: XIAO-YUN LIU ZHI-WU LIAO ZHEN-SONG WANG WU-FAN CHEN

作者单位: School of Automation Engineering,University of Electronic Science and Technology of China, Chengdu 6 School of Applied Mathematics,University of Electronic Science and Technology of China, Chengdu 6100

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 4155-4159

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）