Next Article in Journal
A New Stitching Method for Dark-Field Surface Defects Inspection Based on Simplified Target-Tracking and Path Correction
Previous Article in Journal
Smartphone Biosensor System with Multi-Testing Unit Based on Localized Surface Plasmon Resonance Integrated with Microfluidics Chip
Previous Article in Special Issue
An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering
Open AccessArticle

Automatic Hierarchical Classification of Kelps Using Deep Residual Features

1
Computer Science and Software Engineering, The University of Western Australia, Crawley, WA 6009, Australia
2
School of Biological Sciences and Oceans Institute, The University of Western Australia, Crawley, WA 6009, Australia
3
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Bentley, WA 6845, Australia
4
College of Science, Health, Engineering and Education Murdoch University, Murdoch, WA 6150, Australia
5
Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA 6009, Australia
6
School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 447; https://doi.org/10.3390/s20020447
Received: 21 October 2019 / Revised: 3 January 2020 / Accepted: 8 January 2020 / Published: 13 January 2020
(This article belongs to the Special Issue Imaging Sensor Systems for Analyzing Subsea Environment and Life)
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. View Full-Text
Keywords: deep learning; hierarchical classification; kelp cover; kelps; manual annotation; benthic marine population analysis deep learning; hierarchical classification; kelp cover; kelps; manual annotation; benthic marine population analysis
Show Figures

Figure 1

MDPI and ACS Style

Mahmood, A.; Ospina, A.G.; Bennamoun, M.; An, S.; Sohel, F.; Boussaid, F.; Hovey, R.; Fisher, R.B.; Kendrick, G.A. Automatic Hierarchical Classification of Kelps Using Deep Residual Features. Sensors 2020, 20, 447.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop