Improved Classification of SAR Sea Ice Imagery Based on Segmentation

被引：2

作者：

Yang, Wen ^{[1
]}

He, Chu ^{[1
]}

Cao, Yongfeng ^{[1
]}

Sun, Hong ^{[1
]}

Xu, Xin ^{[1
]}

机构：

[1] Wuhan Univ, Sch Elect Informat, Dept Commun Engn, Signal Proc Lab, Wuhan 430079, Peoples R China

来源：

2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8 | 2006年

关键词：

image classification; watershed segmentation; sea ice; Markov random field model; region adjacency graph; synthtic aperture radar;

D O I：

10.1109/IGARSS.2006.955

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This paper presents a method for semi-supervised classification of polarimetric synthetic aperture radar (PoISAR) sea ice data. The method consists of two steps. In the first stage, a markov random field on region adjacency graph is constructed on the initial watershed oversegmented result. While in the second stage, the Wishart distribution model and maximum a posterior (MAP) are applied as the criterion for obtaining the optimal classification. Good experimental results and less time-consuming are obtained when this method is applied to PoISAR data sets of sea ice in the Beaufort Sea acquired by the airborne AIRSAR.

引用

页码：3727 / 3730

页数：4

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