Statistical analysis of acoustic echoes from underwater meadows in the eutrophic Puck Bay (southern Baltic Sea)

Tęgowski, Jarosław; Gorska, Natalia; Klusek, Z.

doi:10.1016/s0990-7440(03)00015-9

Cited by 32 publications

(13 citation statements)

References 7 publications

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“…At a range of 0.5-30 m and with a pulse length of 0.1 ms, it achieves a resolution of *0.3-0.9 m (according to depth) 9 0.08 m. Research transects were taken perpendicular to the coast, closely following the shores' shape. Based on single beam echo envelopes, statistical features of each clipped ping corresponded to morphological and physical properties of benthic habitats (Tegowski et al 2003). All data were put into a Matlab-based classification system which used probabilistic neural network for analysis (Kruss et al 2008).…”

Section: Study Site Sampling and Conditionsmentioning

confidence: 99%

Microphytobenthos of Arctic Kongsfjorden (Svalbard, Norway): biomass and potential primary production along the shore line

et al. 2010

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During summer 2007, Arctic microphytobenthic potential primary production was measured at several stations around the coastline of Kongsfjorden (Svalbard, Norway) at B5 m water depth and at two stations at five different water depths (5, 10, 15, 20, 30 m). Oxygen planar optode sensor spots were used ex situ to determine oxygen exchange in the overlying water of intact sediment cores under controlled light (ca. 100 lmol photons m -2 s -1 ) and temperature (2-4°C) conditions. Patches of microalgae (mainly diatoms) covering sandy sediments at water depths down to 30 m showed high biomass of up to 317 mg chl a m -2 . In spite of increasing water depth, no significant trend in ''photoautotrophic active biomass'' (chl a, ratio living/dead cells, cell sizes) and, thus, in primary production was measured at both stations. All sites from B5 to 30 m water depth exhibited variable rates of net production from -19 to ?40 mg O 2 m -2 h -1 (-168 to ?360 mg C m -2 day -1 ) and gross production of about 2-62 mg O 2 m -2 h -1 (17-554 mg C m -2 day -1

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Section: Study Site Sampling and Conditionsmentioning

confidence: 99%

Microphytobenthos of Arctic Kongsfjorden (Svalbard, Norway): biomass and potential primary production along the shore line

et al. 2010

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“…Subsequently, these two variables and other indexes built from them were successfully applied to sea floor studies for various reasons (Strong and Service, 2011;Grave et al, 2000;Greenstreet et al, 1997;Siwabessy et al, 2000;Bates and Whitehead, 2001;Satyanarayana et al, 2007;Serpetti et al, 2011). A numbers of computational methods were presented to extract acoustic features, such as the echo duration, skewness, wavelet coefficients, and fractal dimension, for seabed segmentation (Biffard et al, 2010;van Walree et al, 2005;Tegowski et al, 2003). Furthermore, multivariate statistical analysis methods were utilized to achieve supervised or unsupervised classifications of the seabed based on the acoustic features (Tsemahman et al, 1997;Legendre et al, 2002;Preston et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Extraction of visual texture features of seabed sediments using an SVDD approach

Liu

Zhu

et al. 2017

Ocean Engineering

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“…La clasificación de cardúmenes monoespecíficos con información de una única frecuencia acústica se ha realizado mediante un amplio rango de técnicas estadísticas, destacándose los métodos multivariados como análisis de componentes principales y de funciones discriminantes (Nero & Magnuson, 1989;Vray et al, 1990;Scalabrin et al, 1996;Lawson et al, 2001). También se han aplicado métodos de clasificación como redes neuronales artificiales (Haralabous & Georgakarakos, 1996;Simmonds et al, 1996;Cabreira et al, 2009); análisis del vecino más cercano (Richards et al, 1991); conglomerados k-medias (Tegowski et al, 2003); modelos mixtos (Fleischman & Burwen, 2003), método de Kernel (Buelens et al, 2009); métodos árboles de clasificación (Fernandes, 2009). Demer et al (2009) utilizan el análisis estadístico espectral.…”

Section: Introductionunclassified

Clasificacion acustica de anchoveta (Engraulis ringens) y sardina comun (Strangomera bentincki) mediante maquinas de vectores soporte en la zona centro sur de Chile: efecto de la calibracion de los parametros en la matriz de confusion

Robotham

Bosch

Castillo

et al. 2012

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Clasificación acústica de anchoveta (Engraulis ringens) y sardina común(Strangomera bentincki) mediante máquinas de vectores soporte en la zona centro-sur de Chile: efecto de la calibración de los parámetros en la matriz de confusión . Para esto se utilizaron descriptores de cardúmenes extraídos desde ecogramas, que fueron clasificados como morfológicos, batimétricos, energéticos y posicional espacial. Para lograr clasificaciones precisas mediante la utilización de esta metodología, fue necesario optimizar parámetros correspondientes al Kernel-Gaussiano, γ y de penalización del modelo C, mediante el análisis del efecto de la calibración sobre las matrices de confusión resultantes de la clasificación de las especies analizadas. El método SVM ajustó correctamente el 95,3% de los cardúmenes de anchoveta y sardina común. Los parámetros óptimos del Kernel-Gaussiano γ y de penalización C obtenidos mediante la metodología propuesta fueron γ = 450 y C = 0,95, respectivamente. Los parámetros mencionados incidieron de manera importante en la matriz de confusión y los porcentajes de clasificación final, por lo que se sugiere establecer, en aplicaciones futuras de este método, un protocolo experimental de calibración. La sardina común fue la especie con menor error de clasificación en el conjunto de las matrices de confusión. El descriptor correspondiente a profundidad del fondo fue el más sensible al SVM, la segunda variable en importancia es el descriptor distancia a la costa. Palabras clave: máquinas de vectores soporte, clasificación de especies, hidroacústica, peces pelágicos, anchoveta, sardina, Chile. Acoustic classification of anchovy (Engraulis ringens) and sardine (Strangomera bentincki) using support vector machines in central-southernChile: effect of parameter calibration on the confusion matrix ABSTRACT. The support vector machines (SVM) method was used to classify the anchovy (Engraulis ringens) and common sardine (Strangomera bentincki) species detected in south-central Chile by means of acoustic equipment. For this, descriptors of fish schools (morphology, bathymetry, energy, spatial position) extracted from ecograms were used. In order to obtain precise classifications using this methodology, it was necessary to optimize the parameters Gaussian-Kernel γ and penalty term C by analyzing the effect of the calibration on the confusion matrices resulting from the classification of the species under study. The SVM method correctly classified 95.3% of anchovy and sardine schools. The optimal parameters of the GaussianKernel γ and penalty C obtained with the proposed methodology were γ = 450 and C = 0.95. These parameters have an important influence over the confusion matrix and the final classifications percentages, suggesting the development of experimental protocols for calibrating these parameters in future applications of this methodology. In all the confusion matrices, the common sardine showed the lowest classification error. The bottom depth was the descriptor that was most sensitive to the SVM, followed by...

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Statistical analysis of acoustic echoes from underwater meadows in the eutrophic Puck Bay (southern Baltic Sea)

Cited by 32 publications

References 7 publications

Microphytobenthos of Arctic Kongsfjorden (Svalbard, Norway): biomass and potential primary production along the shore line

Microphytobenthos of Arctic Kongsfjorden (Svalbard, Norway): biomass and potential primary production along the shore line

Extraction of visual texture features of seabed sediments using an SVDD approach

Clasificacion acustica de anchoveta (Engraulis ringens) y sardina comun (Strangomera bentincki) mediante maquinas de vectores soporte en la zona centro sur de Chile: efecto de la calibracion de los parametros en la matriz de confusion

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