Optimizing Resolution and Uncertainty in Bathymetric Sonar Systems

Bathymetric sonar systems (whether multibeam or phase-differencing sidescan) contain an inherent trade-off between resolution and uncertainty. Systems are traditionally designed with a fixed spatial resolution, and the parameter settings are optimized to minimize the uncertainty in the soundings within that constraint. By fixing the spatial resolution of the system, current generation sonars operate sub-optimally when the SNR is high, producing soundings with lower resolution than is supportable by the data, and inefficiently when the SNR is low, producing high-uncertainty soundings of little value. Here we propose fixing the sounding measurement uncertainty instead, and optimizing the resolution of the system within that uncertainty constraint. Fixing the sounding measurement uncertainty produces a swath with a variable number of bathymetric estimates per ping, in which each estimate’s spatial resolution is optimized by combining measurements only until the desired depth uncertainty is achieved. When the signal to noise ratio is sufficiently high such that the desired depth uncertainty is achieved with individual measurements, bathymetric estimates are produced at the sonar’s full resolution capability. Correspondingly, a sonar’s resolution is no-longer only considered as a property of the sonar (based on, for example, beamwidth and bandwidth,) but now incorporates geometrical aspects of the measurements and environmental factors (e.g., seafloor scattering strength). Examples are shown from both multibeam and phase- differencing sonar systems

On Importance of Acoustic Backscatter Corrections for Texture-based Seafloor Characterization

Seafloor segmentation and characterization based on local textural properties of acoustic backscatter has been a subject of research since 1980s due to the highly textured appearance of sonar images. The approach consists of subdivision of sonar image in a set of patches of certain size and calculation of a vector of features reflecting the patch texture. Advance of multibeam echosounders (MBES) allowed application of texture-based techniques to real geographical space, and predicted boundaries between acoustic facies became experimentally verifiable. However, acoustic return from uncalibrated MBES produces artifacts in backscatter mosaics, which in turn affects accuracy of delineation. Development of Geocoder allowed creation of more visually consistent images, and reduced the number of factors influencing mosaic creation. It is intuitively clear that more accurate backscatter mosaics lead to more reliable classification results. However, this statement has never been thoroughly verified. It has not been investigated which corrections are important for texture-based characterization and which are not essential. In this paper the authors are investigating the Stanton Banks common dataset. Raw data files from the dataset have been processed by the Geocoder at different levels of corrections. Each processing resulted in a backscatter mosaic demonstrating artifacts of different levels of severity. Mosaics then underwent textural analysis and unsupervised classification using Matlab package SonarClass. Results of seafloor characterization corresponding to varying levels of corrections were finally compared to the one generated by the best possible mosaic (the one embodying all the available corrections), providing an indicator of classification accuracy and giving guidance about which mosaic corrections are crucial for acoustic classification and which could be safely ignored

Establishing a Multibeam Sonar Evaluation Test Bed near Sidney, British Columbia

The Canadian Hydrographic Service (CHS), Naval Oceanographic Office (NAVOCEANO) and the Ocean Mapping Group of the University of New Brunswick (OMG) collaborated on establishing a multibeam sonar test bed in the vicinity of the Institute of Ocean Sciences in Sidney, British Columbia Canada. This paper describes the purpose of the sonar evaluation test bed, the trials and tribulations of two foreign governments collaborating on projects of mutual interest, the evaluation areas and their characteristics for sonar testing, and sample results of sonar evaluations using this test bed. Some target detection comparisons of several systems over a range of artificial sonar targets will also be given

Calibration of multibeam echo sounders: a comparison between two methodologies

Multibeam echo sounders (MBES) are widely used in applications like seafloor imaging, fisheries, and habitat mapping. Calibration of acoustic backscatter is an important aspect of understanding and validating the performance of a MBES. Combined transmit/receive beampattern calibrations were performed on a 200 kHz Reson Seabat 7125 MBES in the acoustic tank of the University of New Hampshire utilizing two different methodologies. The first methodology employs fixed standard target spheres and a high accuracy/high resolution rotation mechanism. This method, similar to that proposed by Foote et al [ Protocols forcalibrating multibeam sonar , J. Acoust. Soc. Am. 117(4), 2005], is designed for a calibrationtank and provides accurate results but requires a large amount of operation time and cannot be performed in situ. The second methodology has been designed for field calibration of MBES. It employs a standard target sphere and a 200 kHz Simrad EK60 split-beam sonar system to provide athwartship and alongship angular information of the target sphere position. This method offers the possibility of field calibration for vessel mounted systems and a significantly reduced operation time, but has a potential reduction in accuracy. In this paper, results from these two methods applied to the same MBES are compared

Development of a fusion adaptive algorithm for marine debris detection within the post-Sandy restoration framework

Recognition of marine debris represent a difficult task due to the extreme variability of the marine environment, the possible targets, and the variable skill levels of human operators. The range of potential targets is much wider than similar fields of research such as mine hunting, localization of unexploded ordnance or pipeline detection. In order to address this additional complexity, an adaptive algorithm is being developing that appropriately responds to changes in the environment, and context. The preliminary step is to properly geometrically and radiometrically correct the collected data. Then, the core engine manages the fusion of a set of statistically- and physically-based algorithms, working at different levels (swath, beam, snippet, and pixel) and using both predictive modeling (that is, a high-frequency acoustic backscatter model) and phenomenological (e.g., digital image processing techniques) approaches. The expected outcome is the reduction of inter-algorithmic cross-correlation and, thus, the probability of false alarm. At this early stage, we provide a proof of concept showing outcomes from algorithms that dynamically adapt themselves to the depth and average backscatter level met in the surveyed environment, targeting marine debris (modeled as objects of about 1-m size). The project relies on a modular software library, called Matador (Marine Target Detection and Object Recognition)

Providing the Third Dimension: High-resolution Multibeam Sonar as a Tool for Archaeological Investigations - An Example from the D-day Beaches of Normandy

In general, marine archaeological investigations begin in the archives, using historic maps, coast surveys, and other materials, to define submerged areas suspected to contain potentially significant historical sites. Following this research phase, a typical archaeological survey uses sidescan sonar and marine magnetometers as initial search tools. Targets are then examined through direct observation by divers, video, or photographs. Magnetometers can demonstrate the presence, absence, and relative susceptibility of ferrous objects but provide little indication of the nature of the target. Sidescan sonar can present a clear image of the overall nature of a target and its surrounding environment, but the sidescan image is often distorted and contains little information about the true 3-D shape of the object. Optical techniques allow precise identification of objects but suffer from very limited range, even in the best of situations. Modern high-resolution multibeam sonar offers an opportunity to cover a relatively large area from a safe distance above the target, while resolving the true three-dimensional (3-D) shape of the object with centimeter-level resolution. A clear demonstration of the applicability of highresolution multibeam sonar to wreck and artifact investigations occurred this summer when the Naval Historical Center (NHC), the Center for Coastal and Ocean Mapping (CCOM) at the University of New Hampshire, and Reson Inc., collaborated to explore the state of preservation and impact on the surrounding environment of a series of wrecks located off the coast of Normandy, France, adjacent to the American landing sectors The survey augmented previously collected magnetometer and high-resolution sidescan sonar data using a Reson 8125 high-resolution focused multibeam sonar with 240, 0.5° (at nadir) beams distributed over a 120° swath. The team investigated 21 areas in water depths ranging from about three -to 30 meters (m); some areas contained individual targets such as landing craft, barges, a destroyer, troop carrier, etc., while others contained multiple smaller targets such as tanks and trucks. Of particular interest were the well-preserved caissons and blockships of the artificial Mulberry Harbor deployed off Omaha Beach. The near-field beam-forming capability of the Reson 8125 combined with 3-D visualization techniques provided an unprecedented level of detail including the ability to recognize individual components of the wrecks (ramps, gun turrets, hatches, etc.), the state of preservation of the wrecks, and the impact of the wrecks on the surrounding seafloor

Seafloor Segmentation Based on Bathymetric Measurements from Multibeam Echosounders Data

Bathymetric data depicts the geomorphology of the seabottom and allows characterization of spatial distributions of apparent benthic habitats. The variability of seafloor topography can be defined as a texture. This prompts for the application of well developed image processing techniques for automatic delineation of regions with clucially different physiographic characteristics. In the present paper histograms of biologically motivated invariant image attributes are used for characterization of local geomorphological feahires. This technique can be naturally applied in a range of spatial scales. Local feature vectors are then submitted to a procedure which divides the set into a number of clusters each representing a distinct type of the seafloor. Prior knowledge about benthic habitat locations allows the use of supervised classification, by training a Suppolt Vector Machine on a chosen data set, and then applying the developed model to a full set. The classification method is shown to perform well on the multibeam echosounder (MBES) data from Piscataqua River, New Hampshire, USA

A New Method for Generation of Soundings from Phase-Difference Measurements

A desirable feature of bathymetric sonar systems is the production of statistically independent soundings allowing a system to achieve its full capability in resolution and object detection. Moreover gridding algorithms such as the Combined Uncertainty Bathymetric Estimator (CUBE) rely on the statistical independence of soundings to properly estimate depth and discriminate outliers. Common methods of filtering to mitigate uncertainty in the signal processing of both multibeam and phase-differencing sidescan systems (curve fitting in zero-crossing detections and differential phase filtering respectively) can produce correlated soundings. Here we propose an alternative method for the generation of soundings from differential phase measurements made by either sonar type to produce statistically independent soundings. The method extracts individual, non-overlapping and unfiltered, phase-difference measurements (from either sonar type) converting these to sonar-relative receive angle, estimates their uncertainty, fixes the desired depth uncertainty level and combines these individual measurements into an uncertainty-weighted mean to achieve the desired depth uncertainty, and no more. When the signal to noise ratio is sufficiently high such that the desired depth uncertainty is achieved with an individual measurement, bathymetric estimates are produced at the sonar’s full resolution capability. When multiple measurements are required, the filtering automatically adjusts to maintain the desired uncertainty level, degrading the resolution only as necessary. Because no two measurements contribute to a single reported sounding, the resulting estimated soundings are statistically independent and therefore better resolve adjacent objects, increase object detectability and are more suitable for statistical gridding methodologies

Uncertainty Modeling for AUV Acquired Bathymetry

Abstract Autonomous Underwater Vehicles (AUVs) are used across a wide range of mission scenarios and from an increasingly diverse set of operators. Use of AUVs for shallow water (less than 200 meters) mapping applications is of increasing interest. However, an update of the total propagated uncertainty TPU model is required to properly attribute bathymetry data acquired from an AUV platform compared with surface platform acquired data. An overview of the parameters that should be considered for data acquired from an AUV platform is discussed. Data acquired in August 2014 using NOAA’s Remote Environmental Measuring UnitS (REMUS) 600 AUV in the vicinity of Portsmouth, NH were processed and analyzed through Leidos’ Survey Analysis and Area Based EditoR (SABER) software. Variability in depth and position of seafloor features observed multiple times from repeat passes of the AUV, and junctioning of the AUV acquired bathymetry with bathymetry acquired from a surface platform are used to evaluate the TPU model and to characterize the AUV acquired data