TY - CHAP
T1 - Pelagic species identification by using a PNN neural network and echo-sounder data
AU - Fontana, Ignazio
AU - Giacalone, Giovanni
AU - Bonanno, Angelo
AU - Mazzola, Salvatore
AU - Basilone, Gualtiero
AU - Genovese, Simona
AU - Aronica, Salvatore
AU - Pissis, Solon
AU - Iliopoulos, Costas S.
AU - Kundu, Ritu
AU - Fiannaca, Antonino
AU - Langiu, Alessio
AU - Lo Bosco, Giosue’
AU - La Rosa, Massimo
AU - Rizzo, Riccardo
PY - 2017/9/11
Y1 - 2017/9/11
N2 - For several years, a group of CNR researchers conducted acoustic surveys in the Sicily Channel to estimate the biomass of small pelagic species, their geographical distribution and their variations over time. The instrument used to carry out these surveys is the scientific echo-sounder, set for different frequencies. The processing of the back scattered signals in the volume of water under investigation determines the abundance of the species. These data are then correlated with the biological data of experimental catches, to attribute the composition of the various fish schools investigated. Of course, the recognition of the fish schools helps to produce very good results, that is very close to the truth about the abundances associated with the various species. In this work, only the acoustic traces of biological monospecific catches, exclusively of two species of pelagic fish. The ecograms where pre-processed using various software tools [1, 2]. For this work, the potential fish schools are detected and isolated using the SHAPES algorithm in Echoview. At the end of the pre-processing phase, the signals are labelled using the two species of pelagic fish: Engraulis encrasicolus and Sardina pilchardus. These labelled signals were used to train a Probabilistic Neural Network (PNN) [3].
AB - For several years, a group of CNR researchers conducted acoustic surveys in the Sicily Channel to estimate the biomass of small pelagic species, their geographical distribution and their variations over time. The instrument used to carry out these surveys is the scientific echo-sounder, set for different frequencies. The processing of the back scattered signals in the volume of water under investigation determines the abundance of the species. These data are then correlated with the biological data of experimental catches, to attribute the composition of the various fish schools investigated. Of course, the recognition of the fish schools helps to produce very good results, that is very close to the truth about the abundances associated with the various species. In this work, only the acoustic traces of biological monospecific catches, exclusively of two species of pelagic fish. The ecograms where pre-processed using various software tools [1, 2]. For this work, the potential fish schools are detected and isolated using the SHAPES algorithm in Echoview. At the end of the pre-processing phase, the signals are labelled using the two species of pelagic fish: Engraulis encrasicolus and Sardina pilchardus. These labelled signals were used to train a Probabilistic Neural Network (PNN) [3].
KW - Classification
KW - Pelagic species identification
KW - Probabilistic neural networks
UR - http://www.scopus.com/inward/record.url?scp=85034249663&partnerID=8YFLogxK
M3 - Other chapter contribution
AN - SCOPUS:85034249663
SN - 9783319685991
VL - 10613 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 454
EP - 455
BT - Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
PB - Springer Verlag
T2 - 26th International Conference on Artificial Neural Networks, ICANN 2017
Y2 - 11 September 2017 through 14 September 2017
ER -