Pubblicazione scientifica

Automatic Hierarchical Classification of Kelps Using Deep Residual Features

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità

RIASSUNTO

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, such as kelps. This paper presents an automatic hierarchical classification method local binary classification as opposed to the conventional flat classification to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs. 57.6% and 77.2% vs. 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.

DATA

Data di pubblicazione: 13/01/2020

AUTORI

MAHMOOD AMMAR
OSPINA ANA GIRALDO
BENNAMOUN MOHAMMED
AN SENJIAN
SOHEL FERDOUS
BOUSSAID FARID
HOVEY RENAE
FISHER ROBERT B
KENDRICK GARY A

ENTE DI AFFERENZA

COMPUTER SCIENCE & SOFTWARE ENG UNIV WESTERN AUSTRALIA CRAWLEY WA 6009 AUSTRALIA MOHAMMED BENNAMOUN UWA EDU AU
SCHOOL OF BIOLOGICAL SCIENCES & OCEANS INST UNIV WESTERN AUSTRALIA CRAWLEY WA 6009 AUSTRALIA ANA GIRALDOOSPINA RSCH UWA EDU AU AGO RENAE HOVEY UWA EDU AU RH GARY KENDRICK UWA EDU AU GAK
SCHOOL OF ELEC ENG COMPUTING & MATHEMATICAL SCIENCES CURTIN UNIV BENTLEY WA 6845 AUSTRALIA S AN CURTIN EDU AU
COLLEGE OF SCIENCE HEALTH ENG & EDUCATION MURDOCH UNIV MURDOCH WA 6150 AUSTRALIA F SOHEL MURDOCH EDU AU
ELECTRICAL ELTNC & COMPUTER ENG UNIV WESTERN AUSTRALIA CRAWLEY WA 6009 AUSTRALIA FARID BOUSSAID UWA EDU AU
SCHOOL OF INFORMATICS UNIV EDINBURGH EDINBURGH EH8 9YL UK RBF INF ED AC UK

RIVISTA

Sensors (Basel, Switzerland), 20(2), 447

IMPACT FACTOR

3,510

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