Morphological and statistical approaches to improve detection in the presence of reverberation

International audienceThe detection of a target echo in a sonar image is usually a difficult task since the reverberation, consisting of a large number of spurious echoes, generates a lot of false alarms. In this paper, we propose two new detectors derived from image processing algorithms. These detectors are respectively based on a morphological and a statistical contrast. Each detector only requires setting a few parameters. This setting is done using some prior knowledge about the data (shape of the emitted signal and the used antenna, characteristics of the reverberation). Nevertheless, an extensive statistical study of the detection performances proves that the proposed methods are robust and that even an imprecise setting of the parameters leads to satisfactory results. Applied to the real data, these detectors and their sequential combination lead to a significant improvement on the performances: The false alarm rate is drastically reduced while the detection probability is preserved. Based on different contrasts, these detectors have complementary behaviors. Therefore, a further improvement is achieved by a fusion of the different results to classify the remaining echoes as whether spurious or true detection

Synthetic Aperture Sonar Imagery: towards a Classification of Underwater Mines in the Mean - Standard Deviation Plane

High resolution images provided by synthetic aperture sonar sensors (SAS) are of great interest, especially for the detection, location and classification of mines laying on the sea bed. For this purpose, this paper proposes a method based on local statistical characteristics of the sonar image. Its goal is to highlight specificities of the echoes of the mines by using a new representation of the data. The results can then be used for a classification of the different kinds of under-water mines