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

Mohamed, Hassan ; Nadaoka, Kazuo ; Nakamura, Takashi

Remote sensing (Basel, Switzerland), 2018-05, Vol.10 (5), p.773 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images
Autor: Mohamed, Hassan ; Nadaoka, Kazuo ; Nakamura, Takashi
Assuntos: Algae ; Algorithms ; Analytical methods ; benthic cover monitoring ; Biodiversity ; Cameras ; Classifiers (sedimentation) ; Coastal zone ; Corals ; Ecological monitoring ; Feature extraction ; Finishes ; Fish ; Global navigation satellite system ; Ground truth ; Habitats ; High resolution ; hybrid classifiers ; Image resolution ; Learning algorithms ; Machine learning ; machine learning algorithms ; Mapping ; Marine resources ; Marine resources management ; Navigation ; Navigation satellites ; Remote sensing ; Resource management ; Satellite imagery ; Sediments ; Support vector machines ; towed underwater video camera ; Underwater
É parte de: Remote sensing (Basel, Switzerland), 2018-05, Vol.10 (5), p.773
Descrição: 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.
Editor: Basel: MDPI AG
Idioma: Inglês

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

Mohamed, Hassan ; Nadaoka, Kazuo ; Nakamura, Takashi

Basel: MDPI AG

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