Full Text:   <2581>

Summary:  <1720>

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

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Chu He

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

-   Go to

Article info.
Open peer comments

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.

@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.

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

Reference

[1]Benboudjema, D., Tupin, F., 2013. Markovian modelling and Fisher distribution for unsupervised classification of radar images. Int. J. Remote Sens., 34(22):8252-8266.

[2]Fukuda, S., 2004. Relating polarimetric SAR image texture to the scattering entropy. Proc. IEEE Int. Geoscience and Remote Sensing Symp., p.2475-2478.

[3]Gambini, J., Mejail, M.E., Jacobo-Berlles, J., et al., 2006. Feature extraction in speckled imagery using dynamic B-spline deformable contours under the $mathcalG^0$ model. Int. J. Remote Sens., 27(22):5037-5059.

[4]He, C., Ahonen, T., Pietikainen, M., 2008. A Bayesian local binary pattern texture descriptor. Proc. 19th Int. Conf. on Pattern Recognition, p.1-4.

[5]He, C., Li, S., Liao, Z., et al., 2013. Texture classification of PolSAR data based on sparse coding of wavelet polarization textons. IEEE Trans. Geosci. Remote Sens., 51(8):4576-4590.

[6]Krylov, V., Moser, G., Serpico, S.B., et al., 2008. Modeling the Statistics of High Resolution SAR Images. Research Report RR-6722, INRIA, France.

[7]Krylov, V., Moser, G., Serpico, S.B., et al., 2009. Dictionary-based probability density function estimation for high-resolution SAR data. Proc. IS&T/SPIE Electronic Imaging, p.72460S.1-72460S.12.

[8]Krylov, V.A., Moser, G., Serpico, S.B., et al., 2013. On the method of logarithmic cumulants for parametric probability density function estimation. IEEE Trans. Image Process., 22(10):3791-3806.

[9]Kuruoglu, E.E., Zerubia, J., 2000. Modelling SAR with a generalisation of the Rayleigh distribution. IEEE Trans. Image Process., 13(4):527-533.

[10]Kuruoglu, E.E., Zerubia, J., 2003. Skewed α-stable distributions for modelling textures. Patt. Recog. Lett., 24(1-3):339-348.

[11]Leung, T., Malik, J., 2001. Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis., 43(1):29-44.

[12]Oliver, C.J., 1986. A model for non-Rayleigh scattering statistics. Opt. Acta: Int. J. Opt., 31(6):701-722.

[13]Oliver, C.J., Quegan, S., 2004. Understanding Synthetic Aperture Radar Images. SciTech Publishing, Stevenage.

[14]Schmid, C., 2001. Constructing models for content-based image retrieval. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.39-45.

[15]Silveira, M., Heleno, S., 2009. Separation between water and land in SAR images using region-based level sets. IEEE Geosci. Remote Sens. Lett., 6(3):471-475.

[16]Singh, J., Datcu, M., 2013. SAR image categorization with log cumulants of the fractional Fourier transform coefficients. IEEE Trans. Geosci. Remote Sens., 51(12): 5273-5282.

[17]Spigai, M., Tison, C., Souyris, J.C., 2011. Time-frequency analysis in high-resolution SAR imagery. IEEE Trans. Geosci. Remote Sens., 49(7):2699-2711.

[18]Varma, M., Zisserman, A., 2002. Classifying images of materials: achieving viewpoint and illumination independence. Proc. European Conf. on Computer Vision, p.255-271.

[19]Varma, M., Zisserman, A., 2005. A statistical approach to texture classification from single images. Int. J. Comput. Vis., 62(1-2):61-81.

[20]Voisin, A., Moser, G., Krylov, V.A., et al., 2010. Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features. Proc. SPIE Remote Sensing, p.585-599.

[21]Xie, X., 2008. A review of recent advances in surface defect detection using texture analysis techniques. Electron. Lett. Comput. Vis. Image Anal., 7(3):1-22.

[22]Yonezawa, C., Watanabe, M., Saito, G., 2012. Polarimetric decomposition analysis of ALOS PALSAR observation data before and after a landslide event. Remote Sens., 4(8):2314-2328.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE