TY - GEN
T1 - Change detection on SAR images using divisive normalization-based image representation
AU - Xu, Qian
AU - Karam, Lina
PY - 2014
Y1 - 2014
N2 - In the context of multi-temporal synthetic aperture radar (SAR) images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or an-thropic disaster. In this paper, we propose a new similarity measure for automatic change detection based on a divisive normalization image representation. The divisive normalization transform (DNT) has been recognized as a successful methodology to model the perceptual sensitivity of biological vision and a useful image representation that significantly reduces statistical dependence of natural images. In this work, we exploit the fact that the histogram of DNT coefficients within wavelet subbands can often be well fitted with a zero-mean Gaussian density function, which is a one-parameter function that allows efficient change detection of SAR images. The proposed change detector is compared to other recent modelbased approaches. Tests on real data show that our detector outperforms previously suggested methods in terms of the rate of false alarm rate and the total error rate.
AB - In the context of multi-temporal synthetic aperture radar (SAR) images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or an-thropic disaster. In this paper, we propose a new similarity measure for automatic change detection based on a divisive normalization image representation. The divisive normalization transform (DNT) has been recognized as a successful methodology to model the perceptual sensitivity of biological vision and a useful image representation that significantly reduces statistical dependence of natural images. In this work, we exploit the fact that the histogram of DNT coefficients within wavelet subbands can often be well fitted with a zero-mean Gaussian density function, which is a one-parameter function that allows efficient change detection of SAR images. The proposed change detector is compared to other recent modelbased approaches. Tests on real data show that our detector outperforms previously suggested methods in terms of the rate of false alarm rate and the total error rate.
KW - Divisive normalization
KW - Gaussian scale mixture
KW - change detection
KW - synthetic aperture radar (SAR) images
UR - http://www.scopus.com/inward/record.url?scp=84905247597&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84905247597&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2014.6854421
DO - 10.1109/ICASSP.2014.6854421
M3 - Conference contribution
AN - SCOPUS:84905247597
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4339
EP - 4343
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -