CLC number: TP75
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-02-15
Cited: 0
Clicked: 8129
Citations: Bibtex RefMan EndNote GB/T7714
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.
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