A review on Stochastic Matched Filter based denoising methods for SAS images despeckling

Abstract

Detection and classification of underwater mines with synthetic aperture sonar (SAS) images is a challenge that can be performed in studying either the echoes or the shadows of mines. But, as any images obtained with a coherent system, SAS images are highly corrupted by the speckle noise, which reduces spatial and radiometric resolutions. So such a noise can be very disturbing for the interpretation and the automatic analysis of SAS images. To reduce the speckle level, filtering methods are generally used but all of them strongly deteriorate either the shadow or the echo of the mine. The purpose of this article is to compare several stochastic matched filter based denoising methods, in order to determine which of them is the most appropriate to enhance both echoes and shadow mines. Results obtained on real SAS data are presented and discussed.