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The performance of an automatic target recognition (ATR) system in the context of naval mine detection is severely affected by the underwater environment. Especially in regions with the presence of sand ripples or mine-sized stones the number of false alarms can become unacceptable high, if the detection algorithm does not account for the type of seafloor. Therefore, a robust way of discriminating...
In this article we are addressing the problem of automatic detection and classification of underwater mines on images generated by a Synthetic Aperture Sonar (SAS). To tackle this problem, we are investigating the use of Machine Learning techniques, in particular Deep Learning. Using this method we faced two challenges, (i) the availability of a sufficient amount of training data to learn the classification...
Change detection methods, consisting in detecting potential changes between two images, representing the same geographical area but acquired at different times, have been widely used in sonar imagery. Such methods are very useful to accurately monitor, potentially low, variations in complex environments. Coherent methods, relying on both amplitude and phase of the backscattered signal, are limited...
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