Robust unsupervised nonparametric change detection of SAR images

This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical cha...

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Bibliographic Details
Main Authors: Garzelli, A., Zoppetti, C., Aiazzi, B., Baronti, S., Alparone, L.
Format: Conference Proceeding
Language:English
Subjects:
Change detection
Feature extraction
Histograms
information-theoretic features
Joints
mean shift algorithm
multi-temporal images
non-parametric methods
Remote sensing
Robustness
Speckle
Synthetic aperture radar
synthetic aperture radar (SAR)
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Description
Summary:This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical changes due to speckle patterns or co-registration inaccuracies. This feature utilizes the scatterplots of the amplitude levels in the two SAR images and applies a fast version of the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. The value of the probability density function (PDF) is translated to a value of conditional information and given to all image pixels originating such modes. Experimental results have been carried out with simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation. The proposed feature exhibits significantly better discrimination capability than both the classical log-ratio (LR) and is particularly robust if applied to SAR images having different processing and/or acquisition angles.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2012.6351111