Abstract:

The ability to discern the characteristics of the seafloor has many applications. Due to minimal visibility, Synthetic Aperture Sonar Imagery (SAS) uses sonar to produce a texture map of the seabed below. In this paper, we discuss an approach to detecting targets from varying seafloor contexts. The approach begins with one or more anomaly detecting prescreeners that use minimal information about targets and that can be applied under various seafloor conditions. In addition, these anomaly detectors see multiple fusion experiments and manipulation to bolster and account for unique target characteristics. Suppressed hits or peaks in the resultant confidence surface, are further processed for scoring. Through ROC curve production and areas under their curves, detection effectiveness becomes simple to distinguish. Attention is paid to determine performance with respect to seafloor type from various locations. The approach is tested on a SAS data collection conducted by the U.S. Navy.

Links:

Citation:

A. Galusha, G. Galusha, J. Keller, A. Zare, “A Target Classification Algorithm for Underwater Synthetic Aperture Sonar Imagery,” in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018. 

@InProceedings{2018SAS,
Title = {A Target Classification Algorithm for Underwater Synthetic Aperture Sonar Imagery},
Author = {Galusha, Aquila P. and Galusha, G. and Keller, James M. and Zare, Alina},
Booktitle = {SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII},
Year = {2018}
}