Automatic target recognition in synthetic aperture sonar images for autonomous mine hunting
conference paper
The future of Mine Countermeasures (MCM) operations lies with unmanned platforms where Automatic
Target Recognition (ATR) is an essential step in making the mine hunting process autonomous. At TNO, a new
ATR method is currently being developed for use on an Autonomous Underwater Vehicle (AUV), using SAS
images of known targets in an operational environment as input. The focus is set on high resolution Synthetic
Aperture Sonar (SAS) input images to follow modern sensor developments. This paper discusses preliminary
results achieved with both trial datasets and simulated data. Various preprocessing methods, feature sets and
classification algorithms are compared on the basis of their classification performance. We conclude that, even
though fine-tuning should further improve the results, the performance of the developed ATR processing chain
is already quite encouraging.
Target Recognition (ATR) is an essential step in making the mine hunting process autonomous. At TNO, a new
ATR method is currently being developed for use on an Autonomous Underwater Vehicle (AUV), using SAS
images of known targets in an operational environment as input. The focus is set on high resolution Synthetic
Aperture Sonar (SAS) input images to follow modern sensor developments. This paper discusses preliminary
results achieved with both trial datasets and simulated data. Various preprocessing methods, feature sets and
classification algorithms are compared on the basis of their classification performance. We conclude that, even
though fine-tuning should further improve the results, the performance of the developed ATR processing chain
is already quite encouraging.
Topics
TNO Identifier
464526
Source title
10th European Conference on Underwater Acoustics ECUA 2010, 5-9 July 2010, Istanbul, Turkey
Files
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