文章基本信息

标题：A new conception of image texture and remote sensing image segmentation based on Markov random field
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作者：Yan Gong ; Ning Shu ; Jili Li 等
期刊名称：Geo-spatial Information Science
印刷版ISSN：1009-5020
电子版ISSN：1993-5153
出版年度：2010
卷号：13
期号：1
页码：16-23
DOI：10.1007/s11806-010-0176-2
出版社：Taylor and Francis Ltd
摘要：The texture analysis is often discussed in image processing domain, but most methods are limited within gray-level image or color image, and the present conception of texture is defined mainly based on gray-level image of single band. One of the essential characters of remote sensing image is multidimensional or even high-dimensional, and the traditional texture conception cannot contain enough information for these. Therefore, it is necessary to pursuit a proper texture definition based on remote sensing images, which is the first discussion in this paper. This paper describes the mapping model of spectral vector in two-dimensional image space using Markov random field (MRF), establishes a texture model of multiband remote sensing image based on MRF, and analyzes the calculations of Gibbs potential energy and Gibbs parameters. Further, this paper also analyzes the limitations of the traditional Gibbs model, prefers a new Gibbs model avoiding estimation of parameters, and presents a new texture segmentation algorithm for hyperspectral remote sensing image later.
关键词：hyperspectral; multispectral; MRF; Gibbs model; texture; segmentation