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Statistical Wavelet Subband Modeling for Multi-temporal SAR Change Detection

Cui, Shiyong and Datcu, Mihai (2012) Statistical Wavelet Subband Modeling for Multi-temporal SAR Change Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (4), pp. 1095-1109. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2012.2200655 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6221963&contentType=Early+Access+Articles&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A4609444%29

Abstract

In the context of multi-temporal SAR change detection for earth monitoring applications, one critical issue is to generate accurate change map. A common method to generate change map is to apply logarithm to the ratio image. However, due to the speckle effect and without consideration of contextual information, it is usually not efficient for accurate change detection. In this paper, an unsupervised change detection method in wavelet domain based on statistical wavelet subband modeling is proposed. The motivation is to capture textures efficiently in wavelet domain. Wavelet transform is applied to decompose the image into multiple scales and probability density function of the coefficient magnitudes of each subband assumed to be Generalized Gaussian Distribution (GGD) and Generalized Gamma Distribution $({rm G}Gamma{rm D})$ are obtained by fast parameter estimation. Closed-form expression of Kullback-Leibler divergence between two corresponding subbands of the same scale is computed and used to generate the change map. This approach is comprehensively evaluated and compared using different parameter setting, different scales, window sizes and estimators. The proposed SAR change detection in wavelet domain shows promising results as texture can be better characterized in wavelet domain than in spatial domain. Through this study, we conclude that the accuracy depends heavily on the estimation methods although the model is important. Both parameter estimation for GGD based on shape equation and parameter estimation for ${rm G}Gamma{rm D}$ using method of log-cumulants (MoLC) in wavelet domain performs quite well.

Item URL in elib:https://elib.dlr.de/76496/
Document Type:Article
Title:Statistical Wavelet Subband Modeling for Multi-temporal SAR Change Detection
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cui, ShiyongRemote sensing technology institute (IMF)UNSPECIFIED
Datcu, MihaiRemote sensing technology institute (IMF)UNSPECIFIED
Date:2012
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:5 (4)
DOI :10.1109/JSTARS.2012.2200655
Page Range:pp. 1095-1109
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Generalized gamma distribution $({rm G}Gamma{rm D})$ , Kullback-Leibler divergence , method of log-cumulants (MoLC) , multi-temporal SAR change detection , synthetic aperture radar (SAR) , undecimated wavelet transform (UWT)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Cui, Shiyong
Deposited On:20 Jul 2012 09:54
Last Modified:08 Mar 2018 18:31

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