Automated segmentation of seafloor bathymetry from multibeam echosounder data using local Fourier histogram texture features

Cutter, G.R.; Rzhanov, Yuri; Mayer, Larry A.

doi:10.1016/s0022-0981(02)00537-3

Cited by 58 publications

(32 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The area was selected because it has been the subject of numerous previous mapping efforts including studies of bedform migration [42], automated segmentation [10], and seafloor scattering models [43,44]. The study area is centered on a shallow, sandy sediment region, determined by multiple means (sonars, divers, and video observations) to be a rippled sand-wave field composed of largely medium to coarse sand and fine shell hash, surrounded by bedrock and gravelly channel sediments [10,42,44,45]. The bedform field is a persistent, elongated feature with its major axis oriented north-south along the main channel axis of the lowermost part of the Piscataqua River estuary [42].…”

Section: Resultsmentioning

confidence: 99%

“…Numerous studies have described approaches to seafloor segmentation and classification using acoustic backscatter data from multibeam sonar [1][2][3][4][5][6][7][8][9] or, alternatively, seafloor bathymetry [10][11][12][13][14][15]. However, there are few studies that have offered general methods for using a machine-focused approach to combine and use the information found in co-located bathymetric digital elevation models (DEMs) and acoustic mosaics [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation

2018

View full text Add to dashboard Cite

A robust and flexible technique to segment seafloor acoustic mapping data by analyzing co-located bathymetric digital elevation models and acoustic backscatter mosaics is presented. The algorithm first uses principles of topographic openness, pattern recognition, and texture classification to identify geomorphic elements of the seafloor or "area kernels", and then derives the final seafloor segmentation by merging or splitting the kernels based on principles of similarity and multi-modality. The output is a collection of homogeneous, non-overlapping seafloor segments of consistent morphology and acoustic backscatter texture. Each labeled segment is enriched by a list of derived, physically-meaningful attributes that can be used for subsequent task-specific analysis.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation

2018

View full text Add to dashboard Cite

show abstract

“…They are very descriptive for determining an efficient performance for an on-line automatic target detector and tracker. Equations (18) and (19) show, respectively, the computation of the number of algorithm instructions for partial sums CA-CFAR presented in [28] and the ACA-CFAR introduced in this work:…”

Section: Quantitative Comparison Of Algorithm Efficienciesmentioning

confidence: 99%

“…� �� * � � (19) Note that the performance index of equation (19) is constant for the same image, depending only on the amount of samples (� � ). Table 2 shows the settings for the automatic detection process with CA-CFAR, PSCA-CFAR and ACA-CFAR.…”

Section: Quantitative Comparison Of Algorithm Efficienciesmentioning

confidence: 99%

“…In effect, while AUV and sonar technology is mature enough for the aforementioned automated tasks and even though many approaches of acoustic images processing are currently available, they still require a strong on-line computational effort to achieve self and environmental perception. These acoustic image processing approaches can be analysed from different points of view regarding their speed, efficiency, resources needed, precision and robustness [12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of an Efficient Approach for Target Tracking from Acoustic Imagery for the Perception System of an Autonomous Underwater Vehicle

Villar

Acosta

Sousa

et al. 2014

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

This article describes the core algorithms of the perception system to be included within an autonomous underwater vehicle (AUV). This perception system is based on the acoustic data acquired from side scan sonar (SSS). These data should be processed in an efficient time, so that the perception system is able to detect and recognize a predefined target. This detection and recognition outcome is therefore an important piece of knowledge for the AUVs dynamic mission planner (DMP). Effectively, the DMP should propose different trajectories, navigation depths and other parameters that will change the robot's behaviour according to the perception system output. Hence, the time in which to make a decision is critical in order to assure safe robot operation and to acquire good quality data; consequently, the efficiency of the on-line image processing from acoustic data is a key issue.Current techniques for acoustic data processing are time and computationally intensive. Hence, it was decided to process data coming from a SSS using a technique that is used for radars, due to its efficiency and its amenability to on-line processing. The engineering problem to solve in this case was underwater pipeline tracking for routine inspections in the off-shore industry. Then, an automatic oil pipeline detection system was developed borrowing techniques from the processing of radar measurements. The radar technique is known as Cell Average -Constant False Alarm Rate (CA -CFAR). With a slight variation of the algorithms underlying this radar technique, which consisted of the previous accumulation of partial sums, a great improvement in computing time and effort was achieved. Finally, a comparison with previous approaches over images acquired with a SSS from a vessel in the Salvador de Bahia bay in Brazil showed the feasibility of using this on-board technique for AUV perception.

show abstract

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

Buscombe

Grams

Kaplinski

2014

J. Geophys. Res. Earth Surf.

View full text Add to dashboard Cite

In this, the second of a pair of papers on the statistical signatures of riverbed sediment in high‐frequency acoustic backscatter, spatially explicit maps of the stochastic geometries (length and amplitude scales) of backscatter are related to patches of riverbed surfaces composed of known sediment types, as determined by georeferenced underwater video observations. Statistics of backscatter magnitudes alone are found to be poor discriminators between sediment types. However, the variance of the power spectrum and the intercept and slope from a power law spectral form (termed the spectral strength and exponent, respectively) successfully discriminate between sediment types. A decision tree approach was able to classify spatially heterogeneous patches of homogeneous sands, gravels (and sand‐gravel mixtures), and cobbles/boulders with 95, 88, and 91% accuracy, respectively. Application to sites outside the calibration and surveys made at calibration sites at different times were plausible based on observations from underwater video. Analysis of decision trees built with different training data sets suggested that the spectral exponent was consistently the most important variable in the classification. In the absence of theory concerning how spatially variable sediment surfaces scatter high‐frequency sound, the primary advantage of this data‐driven approach to classify bed sediment over alternatives is that spectral methods have well‐understood properties and make no assumptions about the distributional form of the fluctuating component of backscatter over small spatial scales.

show abstract

Automated segmentation of seafloor bathymetry from multibeam echosounder data using local Fourier histogram texture features

Cited by 58 publications

References 12 publications

A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation

A Bathymetry- and Reflectivity-Based Approach for Seafloor Segmentation

Evaluation of an Efficient Approach for Target Tracking from Acoustic Imagery for the Perception System of an Autonomous Underwater Vehicle

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

Contact Info

Product

Resources

About