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Deep Image Representations for Coral Image Classification

Research output: Contribution to journalArticle

  • Ammar Mahmood
  • Mohammed Bennamoun
  • Senjian An
  • Ferdous Sohel
  • Farid Boussaid
  • Renae Hovey
  • Gary Kendrick
  • Robert Fisher

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Original languageEnglish
Pages (from-to)121-131
Number of pages21
JournalIEEE Journal of Oceanic Engineering
Issue number1
Early online date12 Jan 2018
Publication statusPublished - Jan 2019


Healthy coral reefs play a vital role in maintaining biodiversity in tropical marine ecosystems. Remote imaging techniques have facilitated the scientific investigations of these intricate ecosystems, particularly at depths beyond 10 meters where SCUBA diving techniques are not time or cost efficient. With millions of digital images of the sea floor collected using Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs), manual annotation of this data by marine experts is a tedious, repetitive and time consuming task. It takes 10-30 minutes for a marine expert to meticulously annotate a single image. Automated technology to monitor the health of the oceans would allow for transformational ecological outcomes by standardizing methods to detect and identify species. This paper aims to automate the analysis of large available AUV imagery by developing advanced deep learning tools for rapid and large-scale automatic annotation of marine coral species. Such an automated technology would greatly benefit marine ecological studies in terms of cost, speed and accuracy. To this end, we propose a deep learning based classification method for coral reefs and report the application of the proposed technique to the automatic annotation of unlabelled mosaics of the coral reef in the Abrolhos Islands, Western Australia. Our proposed method automatically quantified the coral coverage in this region and detected a decreasing trend in coral population, which is in line with conclusions drawn by marine ecologists.

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