Computer classification of sea beds by sonar

Reut, Z.; Pace, Nicholas G.; Heaton, M. J. P.

doi:10.1038/314426a0

Cited by 53 publications

(21 citation statements)

References 4 publications

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“…The high-resolution imaging of underwater environments afforded by sonar has proven useful in a wide range of applications, including habitat mapping [1,2], seabed D. P. Williams (B) NATO Undersea Research Centre, Viale San Bartolomeo 400, 19126 La Spezia (SP), Italy e-mail: williams@nurc.nato.int classification [3][4][5], mine detection [6,7], port protection [8], archaeology [9], and pipeline monitoring [10]. Thanks to breakthroughs in marine robot technology, the sonar data used to address these diverse tasks is invariably collected by an autonomous underwater vehicle (AUV).…”

Section: Introductionmentioning

confidence: 99%

AUV-enabled adaptive underwater surveying for optimal data collection

Williams

2011

Intel Serv Robotics

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A new adaptive strategy for performing data collection with a sonar-equipped autonomous underwater vehicle (AUV) is proposed. The approach is general in the sense that it is applicable to a wide range of underwater tasks that rely on subsequent processing of side-looking sonar imagery. By intelligently allocating resources and immediately reacting to the data collected in-mission, the proposed approach simultaneously maximizes the information content in the data and decreases overall survey time. These improvements are achieved by adapting the AUV route to prevent portions of the mission area from being either characterized by poor image quality or obscured by shadows caused by sand ripples. The peak correlation of consecutive sonar returns is used as a measure for image quality. To detect the presence of and estimate the orientation of sand ripples, a new innovative algorithm is developed. The components of the overall data-driven path-planning algorithm are purposely constructed to permit fast real-time execution with only minimal AUV onboard processing capabilities. Experimental results based on real sonar data collected at sea are used to demonstrate the promise of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

AUV-enabled adaptive underwater surveying for optimal data collection

Williams

2011

Intel Serv Robotics

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“…The research was carried out at the School of Physics, University of Bath, and was sponsored by the British Telecom (initially the Post O$ce) in the period 1981}1985; it resulted in eight reports [4}11]. A paper [12] published in 1988 contains the supporting material and a reference to paper [3], but no mention of any of the reports [4}11] detailing the research carried out in the speci"ed period. The developments in later years which came nearest to the research commented upon were published in 1993 and 1994 [13,14], but with no mention of either papers or reports commented upon [1}12].…”

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confidence: 98%

“…It can be mentioned as a curiosity that the word &&characterization'' was used in that context for the "rst time in 1984, i.e., 10 years previously. The main results of the research carried out in the period 1981}1985 are as follows.The probability density function (PDF) and the cepstrum (the spectrum of the logarithm of spectrum) were found to be most useful for the purpose of the seabed characterization [5,8,9,11]. The power spectrum alone [10] was later found to be more e!ective than originally thought.…”

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confidence: 99%

On the Computer Characterization of Seabeds by Sonars

Reut¹

2000

Journal of Sound and Vibration

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“…Pace and Dyer applied the co-occurrence features to distinguish the physical properties of sedimentary bottoms using sidescan sonar [23]. Reut et al conducted an analysis of one-dimensional image spectra for the classification of six classes of homogeneous sediment type, including mud, clay, sand, gravel, cobbles, and boulders [26]. Pace and Gao continued this work and developed an alternative approach by extracting 1-D spectral features from sidescan data [24].…”

Section: Introductionmentioning

confidence: 99%

Optical and Sonar Image Classification: Wavelet Packet Transform vs Fourier Transform

Tang

Stewart

2000

Computer Vision and Image Understanding

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To develop a noise-insensitive texture classification algorithm for both optical and underwater sidescan sonar images, we study the multichannel texture classification algorithm that uses the wavelet packet transform and Fourier transform. The approach uses a multilevel dominant eigenvector estimation algorithm and statistical distance measures to combine and select frequency channel features of greater discriminatory power. Consistently better performance of the higher level wavelet packet decompositions over those of lower levels suggests that the Fourier transform features, which may be considered as one of the highest possible levels of multichannel decomposition, may contain more texture information for classification than the wavelet transform features. Classification performance comparisons using a set of sixteen Vistex texture images with several level of white noise added and two sets of sidescan sonar images support this conclusion. The new dominant Fourier transform features are also shown to perform much better than the traditional power spectrum method.

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Computer classification of sea beds by sonar

Cited by 53 publications

References 4 publications

AUV-enabled adaptive underwater surveying for optimal data collection

AUV-enabled adaptive underwater surveying for optimal data collection

On the Computer Characterization of Seabeds by Sonars

Optical and Sonar Image Classification: Wavelet Packet Transform vs Fourier Transform

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