Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)

• Main Authors: Fontana I., Giacalone G., Rizzo R., Barra M., Mangoni O., Bonanno A., Basilone G., Genovese S., Mazzola S., Lo Bosco G., Aronica S.
• Other Authors: Fontana, I., Giacalone, G., Rizzo, R., Barra, M., Mangoni, O., Bonanno, A., Basilone, G., Genovese, S., Mazzola, S., Lo Bosco, G., Aronica, S.
• Format: Conference Object
• Language: English
• Published: 2021
• Subjects: Acoustic data; Krill identification; Machine learning for pelagic species classification; Ross Sea; Southern Ocean; Euphausia superba
• Online Access: https://hdl.handle.net/11588/852949; https://doi.org/10.1007/978-3-030-68780-9_7

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 200 kHz). 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.