Fast SAR image change detection using Bayesian approach based difference image and modified statistical region merging.

Han Zhang,Junzheng Wu,Hui Bian,Weiping Ni,Weidong Yan

doi:10.1155/2014/862875

Han Zhang, Junzheng Wu + Show 3 more

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https://doi.org/10.1155/2014/862875

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Abstract

A novel fast SAR image change detection method is presented in this paper. Based on a Bayesian approach, the prior information that speckles follow the Nakagami distribution is incorporated into the difference image (DI) generation process. The new DI performs much better than the familiar log ratio (LR) DI as well as the cumulant based Kullback-Leibler divergence (CKLD) DI. The statistical region merging (SRM) approach is first introduced to change detection context. A new clustering procedure with the region variance as the statistical inference variable is exhibited to tailor SAR image change detection purposes, with only two classes in the final map, the unchanged and changed classes. The most prominent advantages of the proposed modified SRM (MSRM) method are the ability to cope with noise corruption and the quick implementation. Experimental results show that the proposed method is superior in both the change detection accuracy and the operation efficiency.

Highlights

Due to the advantage of gathering imagery in all daytime and all weather conditions, synthetic aperture radar (SAR) images are quite valuable for identification of changes that have occurred after a natural or anthropic disaster [1,2,3,4,5,6,7]
The proposed method is applied to three different SAR image datasets, including two widely used change detection (CD) datasets and our own dataset with two selected areas
A novel fast SAR image change detection method using Bayesian approach based difference image and modified statistical region merging has been presented in this paper

Summary

Introduction

Due to the advantage of gathering imagery in all daytime and all weather conditions, synthetic aperture radar (SAR) images are quite valuable for identification of changes that have occurred after a natural or anthropic disaster [1,2,3,4,5,6,7]. The task of SAR image change detection (CD) suffers from the presence of intrinsic speckle noise. The traditional solution is despeckling with various filters such as the frost filter [4], gamma-MAP filter [5], or the famous nonlocal mean (NLM) filter [6]. The loss of some details inevitably occurs and the despeckling procedure increases the time complexity significantly. Most of the state-ofthe-art methods have to make a compromise between the CD accuracy and the operation efficiency. Further study of automatic SAR image change detection methods is desired

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Results

Conclusion