SAS images denoising: the jointly use of an autoadaptive mean filter and the stochastic matched filter

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Abstract
SAS (Synthetic Aperture Sonar) has been used in sea bed imagery. Indeed, high resolution images provided by SAS are of great interest, especially for the detection, localization or eventually classification of objects lying on sea bed. But, SAS images are highly corrupted by a granular multiplicative noise, called speckle noise, which reduces spatial and radiometric resolutions. For this reason, an automatic analysis of these images is not so evident. A solution can consist on the use of a filtering before process, without a spatial resolution degradation. The purpose of this article is to present a new process consisting on the jointly use of the stochastic matched filter and an autoadaptive mean filter. Furthermore, in order to well preserve the spatial resolution, we propose to use as a criterion for the stochastic matched filter the minimization between the speckle noise local statistics with the removal signal ones, allowing a subimage size adaptation. Results obtained on real SAS data are proposed and compared with those obtained using another stochastic matched filtering based denoising method.

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