Sea ice segmentation using Markov random fields

被引：0

作者：

Yue, B ^{[1
]}

Clausi, DA ^{[1
]}

机构：

[1] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada

来源：

IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS | 2001年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a mean of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework.

引用

页码：1877 / 1879

页数：3

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