Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden

Giosue' Lo Bosco, Simona Genovese, Christopher Nuth, Salvatore Aronica, Giovanni Giacalone, Marco Barra, Monica Calabrò, Ignazio Fontana, Gualtiero Basilone, Riko Noormets, Salvatore Mazzola, Angelo Bonanno, Giuseppa Buscaino, Riccardo Rizzo, Riccardo Rizzo

Risultato della ricerca: Conference contribution

Abstract

Climate change is causing a structural change in Arctic ecosystems, decreasing the effectiveness that the polar regions have in cooling water masses, with inevitable repercussions on the climate and with an impact on marine biodiversity. The Svalbard islands under study are an area greatly influenced by Atlantic waters. This area is undergoing changes that are modifying the composition and distribution of the species present. The aim of this work is to provide a method for the classification of acoustic patterns acquired in the Kongsfjorden, Svalbard, Arctic Circle using multibeam technology. Therefore the general objective is the implementation of a methodology useful for identifying the acoustically reflective 3D patterns in the water column near the Kronebreen glacier. For each pattern identified, characteristic morphological and energetic quantities were extracted. All the information that describes each of the patterns has been divided into more or less homogeneous groupings by means of a K-means partitioning algorithm. The results obtained from clustering suggest that the most correct interpretation is that which divides the data set into 3 distinct clusters, relating to schools of fish. The presence of 3 different schools of fish does not allow us to state that they are 3 different species. The method developed and implemented in this work is a good method for discriminating the patterns present in the water column, obtained from multibeam data, in restricted contexts similar to those of the study area.
Lingua originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagine55-64
Numero di pagine10
Stato di pubblicazionePublished - 2021

Serie di pubblicazioni

NomeLECTURE NOTES IN ARTIFICIAL INTELLIGENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • ???subjectarea.asjc.1700.1700???

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