Johannes Groen

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Synthetic aperture sonar (SAS) has proved to be successful for mine hunting and is now robust for generating high-resolution images over wide swath. The subsequent step in the processing is detection, discriminating between mine-like and non-mine-like objects, which is designed to minimise the number of missed mines so that the system can manage the(More)
An algorithm for the in situ adaptation of the survey route of an autonomous underwater vehicle (AUV) equipped with side-looking sonars is proposed. The algorithm immediately exploits the through-the-sensor data that is collected during the mission in order to ensure that quality data is collected everywhere in the area of interest. By introducing(More)
Autonomous underwater vehicles equipped with high-resolution synthetic aperture sonar (SAS), and automatic target recognition (ATR) algorithms show great potential for the task of search, classify and map. The level of detail recorded by the sonar is typically on the order of hundreds of pixels on an underwater object, valuable for improving classification(More)
In this work, we quantify the relationship between synthetic-aperture length (or equivalently, along-track resolution) and seabed segmentation performance experimentally for real synthetic aperture sonar (SAS) imagery. The seabed segmentation algorithm employed uses wavelet-based features, spectral clustering, and a variational Bayesian Gaussian mixture(More)
A synthetic aperture sonar (SAS) system borne by an autonomous underwater vehicle is state-of-the-art for highresolution sea floor mapping. In the application of automatic target recognition, e.g., for naval mine hunting, the high-resolution SAS images serve as input to a detection and classification post-processing stage, which highly relies on excellent(More)
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