Abstract
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 m where SCUBA diving techniques are not time or cost efficient. With millions of digital images of the seafloor collected using remotely operated vehicles and autonomous underwater vehicles (AUVs), manual annotation of these data by marine experts is a tedious, repetitive, and time-consuming task. It takes 10–30 min 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 unlabeled mosaics of the coral reef in the Abrolhos Islands, W.A., 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.
| Original language | English |
|---|---|
| Number of pages | 11 |
| Journal | IEEE Journal of Oceanic Engineering |
| DOIs | |
| Publication status | E-pub ahead of print - 12 Jan 2018 |
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Deep Image Representations for Coral Image Classification. / Mahmood, Ammar; Bennamoun, Mohammed; An, Senjian; Sohel, Ferdous A.; Boussaid, Farid; Hovey, Renae; Kendrick, Gary A.; Fisher, Robert B.
In: IEEE Journal of Oceanic Engineering, 12.01.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Deep Image Representations for Coral Image Classification
AU - Mahmood, Ammar
AU - Bennamoun, Mohammed
AU - An, Senjian
AU - Sohel, Ferdous A.
AU - Boussaid, Farid
AU - Hovey, Renae
AU - Kendrick, Gary A.
AU - Fisher, Robert B.
PY - 2018/1/12
Y1 - 2018/1/12
N2 - 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 m where SCUBA diving techniques are not time or cost efficient. With millions of digital images of the seafloor collected using remotely operated vehicles and autonomous underwater vehicles (AUVs), manual annotation of these data by marine experts is a tedious, repetitive, and time-consuming task. It takes 10–30 min 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 unlabeled mosaics of the coral reef in the Abrolhos Islands, W.A., 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.
AB - 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 m where SCUBA diving techniques are not time or cost efficient. With millions of digital images of the seafloor collected using remotely operated vehicles and autonomous underwater vehicles (AUVs), manual annotation of these data by marine experts is a tedious, repetitive, and time-consuming task. It takes 10–30 min 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 unlabeled mosaics of the coral reef in the Abrolhos Islands, W.A., 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.
KW - Australia
KW - Classification
KW - coral population
KW - corals
KW - deep learning
KW - Ecosystems
KW - Feature extraction
KW - Image color analysis
KW - marine ecosystems
KW - marine images
KW - Sociology
KW - Statistics
UR - http://www.scopus.com/inward/record.url?scp=85041184436&partnerID=8YFLogxK
U2 - 10.1109/JOE.2017.2786878
DO - 10.1109/JOE.2017.2786878
M3 - Article
JO - IEEE Journal of Oceanic Engineering
JF - IEEE Journal of Oceanic Engineering
SN - 0364-9059
ER -