A Novel Approach Combining KI Criterion and Inverse Gaussian Model to Unsupervised Change Detection in SAR Images

ZHUANG Huifu; DENG Kazhong; YU Mei; FAN Hongdong

doi:10.13203/j.whugis20160079

Volume 43 Issue 2

Feb. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(2): 282-288. > DOI: 10.13203/j.whugis20160079

ZHUANG Huifu, DENG Kazhong, YU Mei, FAN Hongdong. A Novel Approach Combining KI Criterion and Inverse Gaussian Model to Unsupervised Change Detection in SAR Images[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 282-288. DOI: 10.13203/j.whugis20160079

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A Novel Approach Combining KI Criterion and Inverse Gaussian Model to Unsupervised Change Detection in SAR Images

1.
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China

Funds:

The Natural Science Foundation of China 51774270

the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) SKLGP2016K008

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Author Bio:
ZHUANG Huifu, PhD candidate, specializes in change detection of remote sensing images. E-mail: huifuzhuang@163.com
Corresponding author:
DENG Kazhong, PhD, professor. E-mail: kzdeng@cumt.edu.cn
Received Date: May 22, 2016
Published Date: February 04, 2018

Graphical Abstract

Abstract

Abstract

In this context, a novel approach combining inverse Gaussian model (IGM) and the Kittler-Illingworth (KI) criterion has been proposed to carry out tunsupervised change detection in synthetic aperture radar (SAR) images. The minimum error threshold could be computed by exploiting the Bayes decision theory under the assumption that hybrid IGM could describe the distribution of the changed and unchanged class in difference image. Experiments carried out on two sets of multi-temporal SAR images indicate that the proposed approach can effectively estimate the probability density function of the unchanged and changed classes in the difference image and acquire a reasonable threshold for yielding a better change map from the difference image.

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A Novel Approach Combining KI Criterion and Inverse Gaussian Model to Unsupervised Change Detection in SAR Images

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