Challenges in Seafloor Imaging and Mapping With Synthetic Aperture Sonar

Hansen, Roy Edgar; Callow, H.J.; Sabo, Torstein Olsmo; Synnes, Stig Asle Vaksvik

doi:10.1109/tgrs.2011.2155071

Cited by 98 publications

(60 citation statements)

References 24 publications

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“…Sidescan sonar systems are generally too high frequency for buried target localization and classification. SAS images are usually computed in post-processing so that navigation corrections may be applied [1]. The current operational paradigm for the use of both technologies requires a human in the loop for expert image interpretation.…”

Section: Motivationmentioning

confidence: 99%

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Fischell¹

2015

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One of the long term goals of Autonomous Underwater Vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and expert image interpretation. This thesis proposes a vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target for lower cost-per-vehicle sensing and onboard, fully autonomous classification. The contributions of this thesis include the collection of novel high-quality bistatic data sets around spherical and cylindrical targets in situ during the BayEx'14 and Massachusetts Bay 2014 scattering experiments and the development of a machine learning methodology for classifying target shape and estimating orientation using bistatic amplitude data collected by an AUV. To achieve the high quality, densely sampled 3D bistatic scattering data required by this research, vehicle broadside sampling behaviors and an acoustic payload with precision timed data acquisition were developed. Classification was successfully demonstrated for spherical versus cylindrical targets using bistatic scattered field data collected by the AUV Unicorn as a part of the BayEx'14 scattering experiment and compared to simulated scattering models. The same machine learning methodology was applied to the estimation of orientation of aspect-dependent targets, and was demonstrated by training a model on data from simulation then successfully estimating the orientations of a steel pipe in the Massachusetts Bay 2014 experiment. The final models produced from real and simulated data sets were used for classification and parameter estimation of simulated targets in real time in the LAMSS MOOS-IvP simulation environment. fully autonomous underwater target characterization using AUVs. The demonstration of this technology with two field experiments was the highlight of her graduate work, and provided the data required for assessing the value of the methodology in the real world. Her current hobbies, used to maintain sanity during the process of completing this thesis, include hiking, cooking, gardening, music, reading, and thread crafts.

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Section: Motivationmentioning

confidence: 99%

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Fischell¹

2015

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“…To successfully form full resolution SAS images, accurate knowledge of the vehicle navigation (track position and sensor orientation), the ocean environment and in particular the sound velocity, and the seafloor topography must be obtained [11]. This is because the coherent summation of the coarsely estimated reflectivity function from single pings as described in (2) has to be done over exactly the same image coordinates.…”

Section: Angular Resolutionmentioning

confidence: 99%

Use of the minimum variance beamformer in synthetic aperture sonar imaging

Austeng¹,

Jensen²,

Nilsen³

et al. 2013

Proceedings of Meetings on Acoustics

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“…It is commonly assumed that all sonar images are of good quality everywhere, but that is often not the case with real sonar data collected at sea [12]. In particular, image quality often degrades significantly at long range where the effects of multipath manifest [13], or where the more stringent motion correction requirements for successful SAS image formation cannot be satisfied [7].…”

Section: Addressing Poor Image Qualitymentioning

confidence: 99%

A fast physics-based, environmentally adaptive underwater object detection algorithm

Williams

Groen

2011

OCEANS 2011 IEEE - Spain

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Abstract-A new algorithm for the detection of underwater man-made objects in sonar imagery is proposed. The algorithm is made extremely fast by employing a cascaded architecture and by exploiting integral-image representations. As a result, the method makes real-time detection of objects of interest in streaming sonar data collected by an autonomous underwater vehicle feasible. No training data is required because the proposed method is adaptively tailored to the environmental characteristics of the sensed data that is collected in situ. The flexible yet rigorous approach also addresses and overcomes five major limitations that plague the most popular detection algorithms that are in common use. The power and utility of the proposed approach is demonstrated on a large, challenging data set of synthetic aperture sonar imagery collected at sea.

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Challenges in Seafloor Imaging and Mapping With Synthetic Aperture Sonar

Cited by 98 publications

References 24 publications

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Use of the minimum variance beamformer in synthetic aperture sonar imaging

A fast physics-based, environmentally adaptive underwater object detection algorithm

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