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