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1 April 2003 Interferometric side-scan sonar signal denoised by wavelets
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This paper concerns the possibilities that side scan sonar have to determine the bathymetry. New side scan sonars, which are able to image the sea bottom with a high definition, estimate the relief with the same definition as conventional sonar images, using an interferometric multisensors system. Drawbacks concern the accuracy and errors of the numerical altitude model. Interferometric methods use a phase difference to determine a time delay between two sensors. The phase difference belongs to a finite interval (-π, +π), but the time delay between two sensors does not belong to a finite interval: the phase is 2π biased. The used sonar is designend for the use of the vernier technique, which allows to remove this bias. The difficulty comes from interferometric noise, which generates errors on the 2π bias estimation derived from the verier. The traditional way to reduce noise impact on the interferometric signal, is to average data. This method does not preserve the resolution of the bathymetric estimation. This paper presents an attempt to improve the accuracy and resolution of the interferometric signal through a wavelets based method of image despecklization. Traditionally, despecklization is processed on the logarithm of absolute value of the signal. But for this application, the proposed interferometric despecklizaiotn is achieved directly on the interferometric signal by integrating information, guided by the despeckled image. Finally, this multiscale analysis corresponds to an auto adaptive average filtering. A variant of this method is introduced and based on this assumption. This method used the identify function to reconstruct the signal. On the presented results, phase despecklization improves considerably the quality of the interferometric signal in terms of to noise ratio, without an important degradation of resolution.
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Christophe R.B. Sintes, Michel Legris, and Basel Solaiman "Interferometric side-scan sonar signal denoised by wavelets", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003);

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