From Statistical Detection to Decision Fusion: Detection of Underwater Mines in High Resolution SAS Images

ISBN 978-3-902613-48-6Many approaches have been proposed in underwater mine detection and classification using sonar images. The goal is to evaluate a confidence that a pixel belongs to a sought object or to the seabed. In the following, considering the object characteristics (size, reflectivity), we will always assume that the detected objects are actual mines. We propose a detection method structured as a data fusion system. This type of architecture is a smart and adaptive structure: the addition or removal of parameters is easily taken into account, without any modification of the global structure. The inputs of the proposed system are the parameters extracted from an SAS image (statistical in our case). The outputs of the system are the areas detected as potentially including an object

Adaptive Anisotropic Diffusion for Speckle Filtering in SAS Imagery

International audienceThanks to their high resolution, the images provided by synthetic aperture sonar (SAS) are of great interest for the detection and classification of underwater mines lying on the sea bed or buried in the sea floor. As with any acoustic imaging systems, SAS data are highly corrupted by a multiplicative noise, called the speckle, which can be very disturbing for the interpretation and the automatic analysis of the images. In order to reduce this noise and smooth the data, the properties of an anisotropic diffusion filter (ADF) are interesting. In this paper, we propose to adapt the setting of this filter parameters thanks the knowledge provided by an other process. This allows to smooth the speckle noise in the background, while the regions likely to contain any object of interest are preserved

Fusion de données statistiques locales pour la détection en imagerie SAS

International audienceLa détection de mines sous-marines est aujourd'hui un problème stratégique important. Les images fournies par un Sonar à Antenne Synthétique (SAS) sont alors d'un grand intérêt pour la détection et la classification d'objets posés sur le fond ou enfouis dans le sol marin. Cet article présente une méthode de détection de ces objets sous-marins utilisant la fusion, basée sur la théorie de l'évidence et des ensembles flous, de données statistiques locales préalablement extraites de l'image sonar. Ces données sont issues des propriétés statistiques des images SAS aux ordres 1, 2, 3 et 4

Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar Imaging

International audienceDetection of buried underwater objects, and especially mines, is a current crucial strategic task. Images provided by sonar systems allowing to penetrate in the sea floor, such as the synthetic aperture sonars (SASs), are of great interest for the detection and classification of such objects. However, the signal-to-noise ratio is fairly low and advanced information processing is required for a correct and reliable detection of the echoes generated by the objects. The detection method proposed in this paper is based on a data-fusion architecture using the belief theory. The input data of this architecture are local statistical characteristics extracted from SAS data corresponding to the first-, second-, third-, and fourth-order statistical properties of the sonar images, respectively. The interest of these parameters is derived from a statistical model of the sonar data. Numerical criteria are also proposed to estimate the detection performances and to validate the method

Mean / standard deviation representation of sonar images for echo detection : application to SAS images

International audienceThis paper addresses the detection of underwater mines echoes with application to synthetic aperture sonar (SAS)imaging. A detection method based on local first- and second-order statistical properties of the sonar images is proposed. It consists of mapping the data onto the meanstandard deviation plane highlighting these properties. With this representation, an adaptive thresholding of the data enables the separation of the echoes from the reverberation background. The procedure is automated using an entropy criterion (setting of a threshold). Applied on various SAS data sets containing both proud and buried mines, the proposed method positively compares to the conventional amplitude threshold detection method. The performances are evaluated by means of receiver operating characteristic (ROC) curves

Buried mines detection and classification: advanced technologies and signal processing

International audienceIn order to improve the Mine Countermeasures capability, there is a need to investigate new techniques which will enable the detection, localisation and classification of buried mines. This paper deals with results obtained in France, under a sonar program involving GESMA and three academic laboratories. An experimental approach has been preferred. Two techniques are under evaluation: the low frequency Synthetic Aperture Sonar (SAS) mounted on a platform as a side scan sonar dedicated to buried mines detection and a sonar mounted just below a platform, looking vertically at the seabed, dedicated to buried mines classification