0000000000725732

AUTHOR

Olga Mangoni

showing 2 related works from this author

Automatic classification of acoustically detected krill aggregations: A case study from Southern Ocean

2022

Acoustic surveys represent the standard methodology to assess the spatial distribution and abundance of pelagic organisms characterized by aggregative behaviour. The species identification of acoustically observed aggregations is usually performed by taking into account the biological sampling and according to expert-based knowledge. The precision of survey estimates, such as total abundance and spatial distribution, strongly depends on the efficiency of acoustic and biological sampling as well as on the species identification. In this context, the automatic identification of specific groups based on energetic and morphological features could improve the species identification process, allo…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniEnvironmental EngineeringRoss SeaSettore INF/01 - InformaticaEcological Modelingk-meansAcousticKrillInternal validation indicesSoftwareHierarchical clustering
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Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea)

2021

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. Th…

0106 biological sciencesKrillbiologybusiness.industry010604 marine biology & hydrobiologyEuphausiaSettore MAT/01 - Logica MatematicaEuphausia crystallorophiasbiology.organism_classificationSpatial distributionMachine learning for pelagic species classification01 natural sciencesKrill identification010104 statistics & probabilityRoss SeaAcoustic dataArtificial intelligence0101 mathematicsCluster analysisbusinessRelative species abundanceGeologyEnergy (signal processing)Global biodiversityRemote sensing
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