Journal article
Automatic hierarchical classification of kelps using deep residual features
Sensors, Vol.20(2), Article 447
2020
Abstract
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.
Details
- Title
- Automatic hierarchical classification of kelps using deep residual features
- Authors/Creators
- A. Mahmood (Author/Creator) - The University of Western AustraliaA.G. Ospina (Author/Creator) - The University of Western AustraliaM. Bennamoun (Author/Creator) - The University of Western AustraliaS. An (Author/Creator) - Curtin UniversityF. Sohel (Author/Creator) - Murdoch UniversityF. Boussaid (Author/Creator) - The University of Western AustraliaR. Hovey (Author/Creator) - The University of Western AustraliaR.B. Fisher (Author/Creator) - University of EdinburghG.A. Kendrick (Author/Creator) - The University of Western Australia
- Publication Details
- Sensors, Vol.20(2), Article 447
- Publisher
- MDPI
- Identifiers
- 991005540206007891
- Copyright
- © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- Murdoch Affiliation
- College of Science, Health, Engineering and Education
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.2 Marine Biology
- 3.2.570 Coral Reef Ecology
- Web Of Science research areas
- Chemistry, Analytical
- Engineering, Electrical & Electronic
- Instruments & Instrumentation
- ESI research areas
- Chemistry