Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden

• Main Authors: Giacalone G., Lo Bosco G., Barra M., Bonanno A., Buscaino G., Noormets R., Nuth C., Calabro M., Basilone G., Genovese S., Fontana I., Mazzola S., Rizzo R., Aronica S.
• Other Authors: Del Bimbo, A, Cucchiara, R, Sclaroff, S, FarinellaTao Mei Bertini, H, Escalante,J, Vezzani, R., Giacalone, G., Lo Bosco, G., Barra, M., Bonanno, A., Buscaino, G., Noormets, R., Nuth, C., Calabrò, M., Basilone, G., Genovese, S., Fontana, I., Mazzola, S., Rizzo, R., Aronica, S.
• Format: Book Part
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2021
• Subjects: Echo-survey; k-means; Multibeam; Settore MAT/01 - Logica Matematica; Arctic; Kronebreen; Svalbard; Climate change; glacier; Kongsfjord*; Kongsfjorden
• Online Access: http://hdl.handle.net/10447/499446; https://doi.org/10.1007/978-3-030-68780-9_6

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.