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Remote Sens. 2018, 10(5), 773; https://doi.org/10.3390/rs10050773

Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images

1
Department of Geomatics Engineering, Shoubra Faculty of Engineering, Benha University, Cairo 11672, Egypt
2
Department of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, O-okayama W8-13 2-12-1, Meguro-ku, Tokyo, 152-8552, Japan
*
Author to whom correspondence should be addressed.
Received: 28 March 2018 / Revised: 11 May 2018 / Accepted: 16 May 2018 / Published: 17 May 2018
(This article belongs to the Section Ocean Remote Sensing)
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Abstract

Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS) to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF) algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV) ensembles for Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), and Bagging (BAG) classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy. View Full-Text
Keywords: machine learning algorithms; benthic cover monitoring; towed underwater video camera; hybrid classifiers machine learning algorithms; benthic cover monitoring; towed underwater video camera; hybrid classifiers
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Mohamed, H.; Nadaoka, K.; Nakamura, T. Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images. Remote Sens. 2018, 10, 773.

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