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: 6576
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.
@article{title="A statistical distribution texton feature for synthetic aperture radar image classification",
author="Chu He, Ya-ping Ye, Ling Tian, Guo-peng Yang, Dong Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="10",
pages="1614-1623",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601051"
}
%0 Journal Article
%T A statistical distribution texton feature for synthetic aperture radar image classification
%A Chu He
%A Ya-ping Ye
%A Ling Tian
%A Guo-peng Yang
%A Dong Chen
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 10
%P 1614-1623
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601051
TY - JOUR
T1 - A statistical distribution texton feature for synthetic aperture radar image classification
A1 - Chu He
A1 - Ya-ping Ye
A1 - Ling Tian
A1 - Guo-peng Yang
A1 - Dong Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 10
SP - 1614
EP - 1623
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
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.
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