scholarly journals Sampling variance of species identification in fisheries acoustic surveys based on automated procedures associating acoustic images and trawl hauls

2003 ◽  
Vol 60 (3) ◽  
pp. 437-445 ◽  
Author(s):  
Pierre Petitgas ◽  
Jacques Massé ◽  
Pierre Beillois ◽  
Emilie Lebarbier ◽  
Arnaud Le Cann
Keyword(s):  
Species Identification ◽  
Energy Allocation ◽  
Sampling Variance ◽  
Acoustic Image ◽  
Spatial Continuity ◽  
Acoustic Images ◽  
Acoustic Surveys ◽  
Survey Estimates ◽  
Aggregation Step ◽  
Automated Procedures

Abstract During the acoustic surveys of fish stocks, a small number of echo traces are identified to species by fishing. During data analysis, the process of echogram scrutiny leads to allocating echo-trace backscattered energies to species. While the precision of survey estimates is generally based on the spatial variation in the energy, no variance term accounts for species identification and energy allocation. In this paper, the sampling variance of species identification is developed and automated procedures are used allowing energy allocation to be carried out by a non-expert. The procedures are based on the fact that at the sampling stage trawl hauls are linked with particular acoustic images. The procedures have two steps: the classification step corresponds to species identification and the aggregation step to energy allocation. Classification is performed on the identified images and results in defining groups of images and estimating in each the sampling variability of the species identification. Aggregation is performed on non-identified images and results in post-stratifying the data. The estimation (map, abundance and variance per species) is then derived automatically and is conditioned by the post-stratification. Two approaches are followed, one based on the echo-trace characteristics making full use of the echogram (acoustic-image classification) and the other on the spatial continuity of the species composition between trawl hauls (trawl-haul classification). These methods are described and compared. The species-identification variance term is also compared to the spatial variance.

scholarly journals Evaluating acoustic-trawl survey strategies using an end-to-end ecosystem model

2020 ◽  
Author(s):  
Arne Johannes Holmin ◽  
Erik A Mousing ◽  
Solfrid S Hjøllo ◽  
Morten D Skogen ◽  
Geir Huse ◽  
...  
Keyword(s):  
Simulated Data ◽  
Ecosystem Model ◽  
Trawl Survey ◽  
Sampling Variance ◽  
Simulation Framework ◽  
Norwegian Sea ◽  
Atlantic Mackerel ◽  
Fish Migrations ◽  
Survey Estimates ◽  
Trawl Surveys

Abstract Fisheries independent surveys support science and fisheries assessments but are costly. Evaluating the efficacy of a survey before initiating it could save costs. We used the NORWECOM.E2E model to simulate Northeast Atlantic mackerel and Norwegian spring spawning herring distributions in the Norwegian Sea, and we ran vessel transects in silico to simulate acoustic-trawl surveys. The simulated data were processed using standard survey estimation software and compared to the stock abundances in the ecosystem model. Three existing real surveys were manipulated to demonstrate how the simulation framework can be used to investigate effects of changes in survey timing, direction, and coverage on survey estimates. The method picked up general sources of biases and variance, i.e. that surveys conducted during fish migrations are more vulnerable in terms of bias to timing and changes in survey direction than during more stationary situations and that increased effort reduced the sampling variance.

scholarly journals Species identification in seamount fish aggregations using moored underwater video

2012 ◽  
Vol 69 (4) ◽  
pp. 648-659 ◽  
Author(s):  
Richard L. O'Driscoll ◽  
Peter de Joux ◽  
Richard Nelson ◽  
Gavin J. Macaulay ◽  
Adam J. Dunford ◽  
...  
Keyword(s):  
Species Identification ◽  
Deep Water ◽  
Underwater Video ◽  
Orange Roughy ◽  
Video Cameras ◽  
Order Of Magnitude ◽  
Acoustic Surveys ◽  
To Come ◽  
Along Track ◽  
Fish Response

Abstract O'Driscoll, R. L., de Joux, P., Nelson, R., Macaulay, G. J., Dunford, A. J., Marriott, P. M., Stewart, C., and Miller, B. S. 2012. Species identification in seamount fish aggregations using moored underwater video. – ICES Journal of Marine Science, 69: 648–659. Acoustic surveys of New Zealand deep-water seamounts often show fish aggregations up to 150 m high on the summit. Although bottom trawls on the seamount slopes catch predominantly orange roughy (Hoplostethus atlanticus), species composition in the midwater plumes is extremely uncertain. In June 2010, moored underwater video cameras were deployed on the summit of the Morgue seamount (summit depth 890 m), a feature that has been closed to fishing since 2001. Cameras and lights were timed to come on for 2 min every 2 h. Fish response to the mooring was monitored using vessel-mounted echosounders. Moored cameras confirmed that orange roughy were present up to 70 m above the seamount summit. Orange roughy made up 97% of the fish identified from the video. Other species observed included smooth oreo (Pseudocyttus maculatus), spiky oreo (Neocyttus rhomboidalis), deep-water dogfish, cardinalfish (Epigonus spp.), and squid. Total along-track backscatter from the plume varied by a factor of 25 over a period of hours. Peak acoustic densities in the plume (equivalent to 20 orange roughy m−3) were an order of magnitude higher than peak visual estimates (0.64 orange roughy m−3), but relative densities between paired video and acoustic observations were generally consistent.

scholarly journals Evaluating the uncertainty of abundance estimates from acoustic surveys using geostatistical simulations

2009 ◽  
Vol 66 (6) ◽  
pp. 1377-1383 ◽  
Author(s):  
Mathieu Woillez ◽  
Jacques Rivoirard ◽  
Paul G. Fernandes
Keyword(s):  
Acoustic Backscatter ◽  
Fish Length ◽  
Total Abundance ◽  
Geostatistical Model ◽  
Abundance Estimates ◽  
Structural Relationships ◽  
Coefficients Of Variation ◽  
Data Points ◽  
Acoustic Surveys ◽  
Survey Estimates

Abstract Woillez, M., Rivoirard, J., and Fernandes, P. G. 2009. Evaluating the uncertainty of abundance estimates from acoustic surveys using geostatistical simulations. – ICES Journal of Marine Science, 66: 1377–1383. Geostatistical simulations, which can reproduce the spatial variability of a variable, are particularly helpful in estimating the uncertainty associated with the combination of different sources of variability. Acoustic surveys offer an example of such complex situations, where different data (e.g. acoustic backscatter, fish length, and fish age) must be combined to estimate abundance and its associated uncertainty. In this paper, the uncertainty of Scottish herring acoustic-survey estimates is investigated using these techniques. A specific multivariate, geostatistical model is used to describe the structural relationships, which includes highly skewed distributions of the acoustic-backscatter data and incorporates relationships between depth, mean length, and proportions-at-age. Conditional simulations, i.e. geostatistical simulations that honour the data values known at the data points, are used to generate multiple realizations of acoustic backscatter, mean length, and proportions-at-age. These are combined to produce multiple realizations of herring density over the sampled domain. Multiple realizations of total abundance and abundance-at-age are then provided. The uncertainty is assessed using basic statistics to track the significant variations of these values over the period 1989–2005. Higher coefficients of variation (CVs) are found on average for extreme ages (ages 1, 2i, 8, and 9+); otherwise, CVs are mostly around 12% for abundance-at-age and around 10% for total abundance.

scholarly journals A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

2021 ◽  
Vol 9 (4) ◽  
pp. 361
Author(s):  
António José Oliveira ◽  
Bruno Miguel Ferreira ◽  
Nuno Alexandre Cruz
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Feature Detection ◽  
Acoustic Imaging ◽  
Acoustic Image ◽  
Feature Description ◽  
Underwater Acoustic ◽  
Environmental Features ◽  
Acoustic Images ◽  
Underwater Navigation

In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

scholarly journals Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Priyadharsini Ravisankar
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Stationary Wavelet Transform ◽  
Acoustic Image ◽  
Underwater Acoustic ◽  
Acoustic Images ◽  
Frequency Components ◽  
Shrinkage Functions

Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shrinkage functions such as Visushrink and Sureshrink are used for selecting the threshold to remove the undesirable signals in the low frequency component. The high frequency components such as edges and corners are retained. Then the inverse SWT is used for reconstruction of denoised image by combining the modified low frequency components with the high frequency components. The performance measure Peak Signal to Noise Ratio (PSNR) is obtained for various wavelets such as Haar, Daubechies,Coiflet and by changing the thresholding methods.

scholarly journals A method of jellyfish detection based on high resolution multibeam acoustic image

2019 ◽  
Vol 283 ◽  
pp. 04008
Author(s):  
Yang Zhang ◽  
Guijuan Li ◽  
Mingwei Zhang ◽  
Yi Jiang
Keyword(s):  
High Resolution ◽  
Dynamic Characteristic ◽  
Acoustic Method ◽  
Target Strength ◽  
Characteristic Parameters ◽  
Detection Experiment ◽  
Moon Jellyfish ◽  
Acoustic Characteristics ◽  
Acoustic Image ◽  
Acoustic Images

A method based on high resolution multi-beam acoustic image was brought forward for jellyfish detection, and a moon jellyfish detection experiment was conducted on sea. In this experiment, clear acoustic images of individual moon jellyfish were obtained, acoustic method was verified available in moon jellyfish detection. Echo characteristics of individual moon jellyfish were analysed. In the echo image of individual moon jellyfish, four evident echo highlights were found, which were due to the four stomach pouches. Three acoustic characteristic parameters were proposed and estimated to describe jellyfish acoustic characteristics, which were acoustic diameter, acoustic height, and relative target strength. In addition, dynamic characteristic of moon jellyfish was also studied during its pulse motion.

scholarly journals Underwater Target Perception in Local HOS Space

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jue Gao ◽  
Peiyi Zhu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Region Of Interest ◽  
Image Sequences ◽  
Acoustic Imaging ◽  
Acoustic Image ◽  
Acoustic Images ◽  
Underwater Target ◽  
High Order Statistics

In this paper, we propose an underwater target perception architecture, which adopts the three-stage processing including underwater scene acoustic imaging, local high-order statistics (HOS) space conversion, and region-of-interest (ROI) detection. After analysing the problem of the underwater targets represented by the acoustic images, the unique cube structure of the target in local skewness space is noticed, which is used as a clue to develop the ROI detection of underwater scenes. In order to restore the actual appearance of the ROI as much as possible, the focus processing is explored to achieve the target reconstruction. When the target size and number are unknown, using an uncertain theoretical template can achieve a better target reconstruction effect. The performance of the proposed method in terms of SNR, detection rate, and false alarm rate is verified by experiments with several acoustic image sequences. Moreover, target perception architecture is general and can be generalized to a wider range of underwater applications.

Biological Acoustic Microscopy - Living Cells at 37°C and Fixed Cells in Cryogenic Liquids

1982 ◽  
Vol 40 ◽  
pp. 174-177
Author(s):  
J.A. Hildebrand ◽  
D. Rugar ◽  
C.F. Quate
Keyword(s):  
High Frequency ◽  
Acoustic Microscopy ◽  
Single Element ◽  
Acoustic Image ◽  
Acoustic Reflection ◽  
Glass Substrates ◽  
Acoustic Images ◽  
Cryogenic Liquids ◽  
Chick Heart ◽  
Coupling Medium

The scanning reflection acoustic microscope uses high frequency sound to create images with sub-micron resolution. A single-element sapphire lens focuses sound to a diffraction-limited spot in a liquid coupling medium. With the object placed near the focus, the spot of sound is mechanically scanned while the magnitude of the acoustic reflection is recorded for each point on the object. The resulting acoustic image is bright in areas of large acoustic reflection and dark in areas of small acoustic reflection. Acoustic images of biological cells contain information on cellular viscous, elastic and topographic properties. This paper presents acoustic images of living and fixed chick heart fibroblasts grown on glass substrates.

scholarly journals Acoustic species identification of schooling fish

2009 ◽  
Vol 66 (6) ◽  
pp. 1111-1118 ◽  
Author(s):  
Rolf J. Korneliussen ◽  
Yngve Heggelund ◽  
Inge K. Eliassen ◽  
Geir O. Johansen
Keyword(s):  
Species Identification ◽  
Spatial Resolution ◽  
Acoustic Properties ◽  
Acoustic Features ◽  
Schooling Fish ◽  
Nearest Neighbours ◽  
Survey Estimates ◽  
Acoustic Identification ◽  
The Relationship

Abstract Korneliussen, R. J., Heggelund, Y., Eliassen, I. K., and Johansen, G. O. 2009. Acoustic species identification of schooling fish. – ICES Journal of Marine Science, 66: 1111–1118. The development of methods for the acoustic identification of fish is a long-term objective aimed at reducing uncertainty in acoustic-survey estimates. The relative frequency response r(f) measured simultaneously at several frequencies is one of the main acoustic features that characterize the targets, but the relationship between nearest neighbours, school morphology, and environmental and geographical data are also important characteristics in this context. The number of acoustic categories that can be separated with a high spatial resolution is limited by the stochastic nature of the measurements. Because the acoustic categorization of larger ensembles is more reliable than for single targets, spatial smoothing of the backscattering within the school boundaries before that process allows the separation of more categories than is possible with the raw, highly resolved data. Using the mean r(f) of an entire school gives even more reliable categorization, but determining whether or not the school is monospecific sets a new challenge. This problem is evaluated here. The methods are tested and verified. Identification of acoustic categories with similar acoustic properties is done for schooling fish, although the results have limited spatial resolution. The reliability of the categorization is further improved when knowledge of school morphology and geographical distribution of the species are taken into account.

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