Abstract
Accurate identification of aquatic organisms and their numerical abundance calculation using echo detection techniques remains a great challenge for marine researchers. A software architecture for echo data processing is presented in this article. Within it, it is discussed how to obtain energetic, morphometric and bathymetric fish school descriptors to accurately identify different fish-species. To accomplish this task it was necessary to have a development platform that allowed reading echo data from a particular echosounder, to detect fish aggregations and then to calculate fish school descriptors that would be used for fish-species identification, in an automatic way. This article also describes thoroughly the digital processing algorithms for this automatic detection and classification, as well as the automatic process required for surface and bottom line detection, which is necessary to determine the exploration range. These algorithms are implemented within the ECOPAMPA software, which is the first Argentinean system for marine species identification. Finally, a comparative result over experimental data of ECOPAMPA against EchoviewTM Software Pty Ltd (formerly Myriax Software Pty Ltd), is carefully examined.
Highlights
Since hydroacoustics can provide a continuous high resolution sampling of a large volumes of water over a short period of time, compared to other sampling technologies such as electromagnetics and optics, it is currently the most efficient tool to remotely study the aquatic environment (Simmonds and MacLennan, 2006)
This approach consists of a group of sequential procedures based on Digital Image Processing (DIP), including image segmentation, morphological operations, edge detection, image representation, Binary Large Object (BLOB) analysis and classification with Artificial Neural Network (ANN) (Gonzalez and Woods, 2001; Haykin, 2009; Pratt, 2001; Qiusheng et al, 2014; Russ, 2000)
The mentioned difference comes from the digital image processing used in our approach, which leads to a more accurate definition of schools
Summary
Since hydroacoustics can provide a continuous high resolution sampling of a large volumes of water over a short period of time, compared to other sampling technologies such as electromagnetics and optics, it is currently the most efficient tool to remotely study the aquatic environment (Simmonds and MacLennan, 2006). Today it is possible to generate a great amount of data during an acoustic survey, usually several GBytes per day. For this reason the extraction of useful information is delayed for a post-processing analysis stage. During a regular fish stock assessment it is possible to detect hundreds or thousands of fish schools. A profuse literature has been published describing the results of applying image processing to recognize fish schools silhouette's automatically, allowing the assessment of interesting information about size, shape, structure and position in the water column of the aggregations (Coetzee, 2000; Diner, 2001; Lefort et al, 2012; Scalabrin and Masse, 1993)
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