Higher Order Statistics for the Detection of Underwater Mines in SAS Imagery

Synthetic Aperture Sonar (SAS) imagery is largely used in detection, location, and classification of underwater mines laying or buried in the sea bed. This paper proposes a detection method using Higher Order Statistics (HOS) on SAS images. The proposed method can be divided into two steps. Firstly, the HOS (Skewness and Kurtosis) are locally estimated using a square sliding computation window. In a second step, the results are focused by a matched filtering. This enables the precise location of the objects. This method is tested on real SAS data containing both underwater mines laying on the seabed and buried objects

On the Use of Higher Order Statistics in SAS Imagery

Synthetic Aperture Sonar (SAS) imagery is largely used in detection, location and classification of underwater mines laying or buried in the sea bed. This paper proposes a detection method using Higher Order Statistics (HOS) on SAS images. The proposed method can be divided into two steps. Firstly, the HOS (Skewness and Kurtosis) are locally estimated using a square sliding computation window. In a second step, the results are focused by a correlation process. This enables the precise location of the objects. This method is tested on real SAS data containing both underwater mines laying on the sea bed and buried objects

Scalar Image Processing Filters for Speckle Reduction on Synthetic Aperture Sonar Images

High resolution images provided by synthetic aperture sonar sensors (SAS) are of great interest, especially for the detection, location and classification of mines lying on the sea bed. But these data obtained by an active imagery system are highly corrupted by a noise called the speckle. To reduce this noise and suppress the spurious reflections it generates on the images, we propose to use different algorithms based on image processing techniques. Several image processing filters are tested and discussed. A comparative study of the results obtained on real SAS data is presented. The different filters are evaluated both visually and in terms of variance reduction

Automated Segmentation of SAS Images using the Mean - Standard Deviation Representation

A segmentation method of synthetic aperture sonar (SAS) images is presented, in order to highlight some characteristics (number, position, shape, ...) of underwater mines echoes. This segmentation method is based on statistical characteristics of the sonar images, highlighted by the mean – standard deviation plane. It is automated by using an entropy criterion

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

Use of Statistical Hypothesis Test for Mines Detection in SAS Imagery

International audienceSynthetic Aperture Sonar (SAS) imagery is currently used in order to detect underwater mines laying on or buried in the sea bed. But the low signal to noise ratio characterizing these images leads to a high number of false alarms. In this paper, a new method of detection based on a statistical hypothesis test is presented. The proposed method can be divided into two main steps. Firstly, a statistical model of the speckle noise is described. A statistical hypothesis test is then performed and an evaluation of the performances is proposed

Fusion of Local Statistical Detectors in SAS Imagery Classification

International audienceDetection of underwater mines is a present crucial strategic task. The images provided by Synthetic Aperture Sonar (SAS) are then of great interest for the detection and classification of objects lying on the sea floor or buried in the sea bed. This paper proposes a detection method based on data fusion, using local statistical characteristics extracted from the SAS data. These values come from first, second, third, and fourth order statistical properties of the sonar images

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

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