From statistical detection to decision fusion: detection of underwater mines in high resolution SAS images

Frederic Maussang (1) , Jocelyn Chanussot (2) , Michèle Rombaut (3) , Maud Amate (4)

1 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192)
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
3 GIPSA-GPIG - GIPSA - Géométrie, Perception, Images, Geste
4 DGA/DET/GESMA - Groupe d'Etudes Sous-Marines de l'Atlantique

Many 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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Frederic Maussang, Jocelyn Chanussot, Michèle Rombaut, Maud Amate. From statistical detection to decision fusion: detection of underwater mines in high resolution SAS images. Sergio Rui Silva,. Advances in Sonar Technology, IN-TECH (Open Acces Book), pp.111 - 150, 2009. ⟨hal-00367598⟩