A New Conception of Image Texture and Remote Sensing Image Segmentation Based on Markov Random Field  

作　　者：GONG Yan SHU Ning LI Jili LIN Liqun LI Xue 

机构地区：[1]School of Remote Sensing and Information Engineering, Wuhan University,129 Luoyu Road, Wuhan 430079, China [2]State Key Laboratory of Inforination Engineering in Surveying Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China [3]Faculty of Environmental Studies, York University, 2275 Bayview Avenue Toronto, Ontario,Canada [4]Faculty of Resource and Environmental Science, Hubei University, Wuhan 430062, China

出　　处：《Geo-Spatial Information Science》2010年第1期16-23,共8页地球空间信息科学学报（英文）

基　　金：Supported by the National Key Basic Research and Development Program(No.2006CB701303, No. 2004CB318206)

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 hy-perspectral remote sensing image later.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 hy-perspectral remote sensing image later.

关 键 词：hyperspectral multispectral MRF Gibbs model texture segmentation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] V35[自动化与计算机技术—计算机科学与技术]