Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)
This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200kHz). The goal of classifying the gregarious species represents a time-consuming tas...
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ftunivpalermo:oai:iris.unipa.it:10447/499443 2024-02-11T10:03:36+01:00 Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea) Fontana I. Giacalone G. Rizzo R. Barra M. Mangoni O. Bonanno A. Basilone G. Genovese S. Mazzola S. Lo Bosco G. Aronica S. Fontana, I Giacalone,G Rizzo,R Barra, M Mangoni, O Bonanno, A Basilone, G Genovese,S Mazzola S Lo Bosco, G Aronica, S. Fontana I. Giacalone G. Rizzo R. Barra M. Mangoni O. Bonanno A. Basilone G. Genovese S. Mazzola S. Lo Bosco G. Aronica S. 2021 http://hdl.handle.net/10447/499443 https://doi.org/10.1007/978-3-030-68780-9_7 eng eng Springer Science and Business Media Deutschland GmbH info:eu-repo/semantics/altIdentifier/isbn/978-3-030-68779-3 info:eu-repo/semantics/altIdentifier/isbn/978-3-030-68780-9 ispartofbook:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 25th International Conference on Pattern Recognition Workshops, ICPR 2020 volume:12666 firstpage:65 lastpage:74 numberofpages:10 serie:LECTURE NOTES IN ARTIFICIAL INTELLIGENCE http://hdl.handle.net/10447/499443 doi:10.1007/978-3-030-68780-9_7 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85103288215 info:eu-repo/semantics/closedAccess Acoustic data Krill identification Machine learning for pelagic species classification Ross Sea Settore MAT/01 - Logica Matematica info:eu-repo/semantics/bookPart 2021 ftunivpalermo https://doi.org/10.1007/978-3-030-68780-9_7 2024-01-23T23:30:51Z This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200kHz). The goal of classifying the gregarious species represents a time-consuming task and is accomplished by using differences and/or thresholds estimated on the energy features of the insonified targets. Conversely, our methodology takes into account energy, morphological and depth features of echo data, acquired at different frequencies. Internal validation indices of clustering were used to verify the ability of the clustering in recognizing the correct number of species. The proposed approach leads to the characterization of the two krill species (Euphausia superba and Euphausia crystallorophias), providing reliable indications about the species spatial distribution and relative abundance. Book Part Euphausia superba Ross Sea Southern Ocean IRIS Università degli Studi di Palermo Ross Sea Southern Ocean 65 74 |
institution |
Open Polar |
collection |
IRIS Università degli Studi di Palermo |
op_collection_id |
ftunivpalermo |
language |
English |
topic |
Acoustic data Krill identification Machine learning for pelagic species classification Ross Sea Settore MAT/01 - Logica Matematica |
spellingShingle |
Acoustic data Krill identification Machine learning for pelagic species classification Ross Sea Settore MAT/01 - Logica Matematica Fontana I. Giacalone G. Rizzo R. Barra M. Mangoni O. Bonanno A. Basilone G. Genovese S. Mazzola S. Lo Bosco G. Aronica S. Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea) |
topic_facet |
Acoustic data Krill identification Machine learning for pelagic species classification Ross Sea Settore MAT/01 - Logica Matematica |
description |
This work presents a computational methodology able to automatically classify the echoes of two krill species recorded in the Ross sea employing scientific echo-sounder at three different frequencies (38, 120 and 200kHz). The goal of classifying the gregarious species represents a time-consuming task and is accomplished by using differences and/or thresholds estimated on the energy features of the insonified targets. Conversely, our methodology takes into account energy, morphological and depth features of echo data, acquired at different frequencies. Internal validation indices of clustering were used to verify the ability of the clustering in recognizing the correct number of species. The proposed approach leads to the characterization of the two krill species (Euphausia superba and Euphausia crystallorophias), providing reliable indications about the species spatial distribution and relative abundance. |
author2 |
Fontana, I Giacalone,G Rizzo,R Barra, M Mangoni, O Bonanno, A Basilone, G Genovese,S Mazzola S Lo Bosco, G Aronica, S. Fontana I. Giacalone G. Rizzo R. Barra M. Mangoni O. Bonanno A. Basilone G. Genovese S. Mazzola S. Lo Bosco G. Aronica S. |
format |
Book Part |
author |
Fontana I. Giacalone G. Rizzo R. Barra M. Mangoni O. Bonanno A. Basilone G. Genovese S. Mazzola S. Lo Bosco G. Aronica S. |
author_facet |
Fontana I. Giacalone G. Rizzo R. Barra M. Mangoni O. Bonanno A. Basilone G. Genovese S. Mazzola S. Lo Bosco G. Aronica S. |
author_sort |
Fontana I. |
title |
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea) |
title_short |
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea) |
title_full |
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea) |
title_fullStr |
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea) |
title_full_unstemmed |
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea) |
title_sort |
unsupervised classification of acoustic echoes from two krill species in the southern ocean (ross sea) |
publisher |
Springer Science and Business Media Deutschland GmbH |
publishDate |
2021 |
url |
http://hdl.handle.net/10447/499443 https://doi.org/10.1007/978-3-030-68780-9_7 |
geographic |
Ross Sea Southern Ocean |
geographic_facet |
Ross Sea Southern Ocean |
genre |
Euphausia superba Ross Sea Southern Ocean |
genre_facet |
Euphausia superba Ross Sea Southern Ocean |
op_relation |
info:eu-repo/semantics/altIdentifier/isbn/978-3-030-68779-3 info:eu-repo/semantics/altIdentifier/isbn/978-3-030-68780-9 ispartofbook:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 25th International Conference on Pattern Recognition Workshops, ICPR 2020 volume:12666 firstpage:65 lastpage:74 numberofpages:10 serie:LECTURE NOTES IN ARTIFICIAL INTELLIGENCE http://hdl.handle.net/10447/499443 doi:10.1007/978-3-030-68780-9_7 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85103288215 |
op_rights |
info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.1007/978-3-030-68780-9_7 |
container_start_page |
65 |
op_container_end_page |
74 |
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1790599888517464064 |