Full Text:   <856>

Summary:  <193>

CLC number: TP753

On-line Access: 2017-12-04

Received: 2016-03-05

Revision Accepted: 2016-09-04

Crosschecked: 2017-10-10

Cited: 0

Clicked: 1753

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Chu He

http://orcid.org/0000-0003-3662-5769

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.10 P.1614-1623

http://doi.org/10.1631/FITEE.1601051


A statistical distribution texton feature for synthetic aperture radar image classification


Author(s):  Chu He, Ya-ping Ye, Ling Tian, Guo-peng Yang, Dong Chen

Affiliation(s):  School of Electronic Information, Wuhan University, Wuhan 430072, China; more

Corresponding email(s):   lingtianwhu@126.com

Key Words:  Synthetic aperture radar, Statistical distribution, Parameter estimation, Image classification


Chu He, Ya-ping Ye, Ling Tian, Guo-peng Yang, Dong Chen. A statistical distribution texton feature for synthetic aperture radar image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1614-1623.

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author="Chu He, Ya-ping Ye, Ling Tian, Guo-peng Yang, Dong Chen",
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publisher="Zhejiang University Press & Springer",
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%A Guo-peng Yang
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A1 - Chu He
A1 - Ya-ping Ye
A1 - Ling Tian
A1 - Guo-peng Yang
A1 - Dong Chen
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DOI - 10.1631/FITEE.1601051


Abstract: 
We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram statistics. Experimental results on TerraSAR data demonstrate the effectiveness of the proposed s-texton feature.

用于SAR图像分类的统计分布基元特征

概要:本文提出了一种新颖的用于合成孔径雷达图像分类的统计分布基元(S基元)特征。受传统基元特征、纹理分析框架及SAR图像中统计分布重要性的启发,本文基于统计分布的参数估计可以替代传统SAR图像纹理分析中滤波器操作这一想法提出了S基元特征。在S基元特征的提取过程中,本文采用了一些策略,如预处理、空间网格化、参数估计、基元集群、直方图统计。在TerraSAR数据上的实验结果验证了本文S基元特征的有效性。

关键词:合成孔径雷达;统计分布;参数估计;图像分类

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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