Full Text:   <616>

Summary:  <237>

CLC number: TP75

On-line Access: 2019-03-11

Received: 2017-07-11

Revision Accepted: 2017-08-28

Crosschecked: 2019-02-15

Cited: 0

Clicked: 3150

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Santiago Ruzi-arenas

https://orcid.org/0000-0002-4018-7370

Xiao-qiang She

http://orcid.org/0000-0003-3063-770X

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.2 P.253-264

http://doi.org/10.1631/FITEE.1700462


Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery


Author(s):  Ting-ting Jin, Xiao-qiang She, Xiao-lan Qiu, Bin Lei

Affiliation(s):  Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China; more

Corresponding email(s):   jintingting13@mails.ucas.ac.cn, sxq@mail.ustc.edu.cn

Key Words:  Intertidal classification, Polarimetric synthetic aperture radar, Finite mixture model, Markov random field, Generalized extreme value model


Ting-ting Jin, Xiao-qiang She, Xiao-lan Qiu, Bin Lei. Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 253-264.

@article{title="Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery",
author="Ting-ting Jin, Xiao-qiang She, Xiao-lan Qiu, Bin Lei",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="2",
pages="253-264",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700462"
}

%0 Journal Article
%T Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery
%A Ting-ting Jin
%A Xiao-qiang She
%A Xiao-lan Qiu
%A Bin Lei
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 2
%P 253-264
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700462

TY - JOUR
T1 - Intertidal area classification with generalized extreme value distribution and Markov random field in quad-polarimetric synthetic aperture radar imagery
A1 - Ting-ting Jin
A1 - Xiao-qiang She
A1 - Xiao-lan Qiu
A1 - Bin Lei
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 2
SP - 253
EP - 264
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700462


Abstract: 
Classification of intertidal area in synthetic aperture radar (SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value (GEV) statistical model of the polarization features and the markov random field (MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.

基于广义极值分布和马尔科夫随机场的全极化合成孔径雷达图像潮间带区域地物分类方法

摘要:考虑到潮汐波动的复杂性和剧烈性,合成孔径雷达(SAR)图像的潮间带区域地物分类是一个重要且具有挑战性的问题。潮间带区域地物分类难点在于,潮间带有很大比例区域经常被海水淹没,使得利用空间信息的统计建模方法常常失效。考虑到极化熵和各向异性在潮间带特征描述中的重要作用,提出一种新的无监督地物分类算法。该算法将广义极值分布模型(GEV)的极化特征与马尔可夫随机场(MRF)的平滑滤波结合,并加入拟合优度检验,以确定统计模型各组成部分的显著性。将极化熵和各向异性的分类结果有效结合,得到最终分类结果。最后,基于中国高分三号SAR卫星获得的全极化数据进行实验,实验结果证明所提方法的可行性和优越性。

关键词:潮间带区域地物分类;极化合成孔径雷达;有限混合模型;马尔可夫随机场;广义极值分布模型

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

Reference

[1]Akaike H, 1973. Information theory and an extension of the maximum likelihood principle. Proc 2nd Int Symp on Information Theory, p.267-281.

[2]Boerner WM, 1990. Introduction to radar polarimetry—with assessments of the historical development and of the current state-of-the-art. In: Guran AS, Mittra R, Moser PJ (Eds.), Electromagnetic Wave Interactions. World Scientific, Singapore, p.139-214.

[3]Cloude SR, 1995. An entropy based classification scheme for polarimetric SAR data. Int Geoscience and Remote Sensing Symp, p.2000-2002.

[4]Cloude SR, Pottier E, 1996. A review of target decomposition theorems in radar polarimetry. IEEE Trans Geosci Remote Sens, 34(2):498-518.

[5]Ding H, Huang Y, Liu N, et al., 2015. Modeling of sea spike events with generalized extreme value distribution. European Radar Conf, p.113-116.

[6]Doulgeris AP, Akbari V, Eltoft T, 2012. Automatic PolSAR segmentation with the -distribution and Markov random fields. European Conf on Synthetic Aperture Radar, p.183-186.

[7]Geng XM, Li XM, Velotto D, et al., 2016. Study of the polarimetric characteristics of mud flats in an intertidal zone using C- and X-band spaceborne SAR data. Remote Sens Environ, 176:56-68.

[8]Inglada J, Garello R, 2000. Underwater bottom topography estimation from SAR images by regularization of the inverse imaging mechanism. Proc IEEE Int Geoscience and Remote Sensing Symp, p.1848-1850.

[9]Kim DJ, Park SE, Lee HS, et al., 2009. Investigation of multiple frequency polarimetric SAR signal backscattering from tidal flats. IEEE Int Geoscience and Remote Sensing Symp, p.896-899.

[10]Kim JE, Park SE, Kim DJ, et al., 2011. Recent advances in POL(in)SAR remote sensing and stress-change monitoring of wetlands with applications to the Sunchon Bay Tidal Flats. European Conf on Synthetic Aperture Radar, p.1-3.

[11]Lee H, Chae H, Cho SJ, 2011. Radar backscattering of intertidal mudflats observed by Radarsat-1 SAR images and ground-based scatterometer experiments. IEEE Trans Geosci Remote Sens, 49(5):1701-1711.

[12]Li HC, Hong W, Wu YR, 2007. Generalized gamma distribution with MoLC estimation for statistical modeling of SAR images. 1st Asian and Pacific Conf on Synthetic Aperture Radar, p.525-528.

[13]Li SZ, 2009. Markov Random Field Modeling in Image Analysis. Springer, London, UK.

[14]Li Z, Heygester G, Notholt J, 2012. Topographic mapping of Wadden Sea, with SAR images and waterlevel model data. IEEE Int Geoscience and Remote Sensing Symp, p.2645-2648.

[15]Li Z, Heygester G, Notholt J, 2013. The topography comparsion between the year 1999 and 2006 of German tidal flat wadden sea analyzing SAR images with waterline method. IEEE Int Geoscience and Remote Sensing Symp, p.2443-2446.

[16]Li Z, Heygster G, Notholt J, 2014. Intertidal topographic maps and morphological changes in the German wadden sea between 1996–1999 and 2006–2009 from the waterline method and SAR images. IEEE J Sel Top Appl Earth Observ Remote Sens, 7(8):3210-3224.

[17]Park SE, Moon WM, Kim DJ, 2009. Estimation of surface roughness parameter in intertidal mudflat using airborne polarimetric SAR data. IEEE Trans Geosci Remote Sens, 47(4):1022-1031.

[18]Peli E, 1990. Contrast in complex images. J Opt Soc Am A, 7(10):2023-2040.

[19]Pottier E, 1998. Unsupervised classification scheme and topography derivation of POLSAR data on the H/A/a polarimetric decomposition theorem. Proc 4th Int Workshop on Radar Polarimetry, p.535-548.

[20]She X, Qiu X, Lei B, et al., 2017. A classification method based on polarimetric entropy and GEV mixture model for intertidal area of PolSAR image. J Rad, 6(5):554-563 (in Chinese).

[21]Uebersax JS, 1982. A generalized Kappa coefficient. Educ Psychol Meas Sage Publ, 42(1):181-183.

[22]van del Wal D, Herman PMJ, van den Dool AW, 2005. Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery. Remote Sens Environ, 98(1):96-109.

[23]Won ES, Ouchi K, Yang CS, 2013. Extraction of underwater laver cultivation nets by SAR polarimetric entropy. IEEE Geosci Remote Sens Lett, 10(2):231-235.

[24]Wu Y, Ji K, Yu W, et al., 2008. Region-based classification of polarimetric SAR images using Wishart MRF. IEEE Geosci Remote Sens Lett, 5(4):668-672.

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 - Journal of Zhejiang University-SCIENCE