Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

Mohamed, Hassan; Nadaoka, Kazuo; Nakamura, Takashi

doi:10.3390/rs14081818

Cited by 8 publications

(4 citation statements)

References 67 publications

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“…Underwater video surveys have recently gained significant interest for benthic habitat identification and classification (Keogh et al, 2022;Mohamed et al, 2022;Ternon et al, 2022). Videos are usually recorded using high-definition (HD) cameras mounted on remotely operated vehicles (ROVs) (Robert et al, 2017;Keogh et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

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3d Mapping of Benthic Habitat Using Xgboost and Structure From Motion Photogrammetry

Morsy,

Yánez Suárez,

Robert

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Benthic habitats mapping is essential to the management and conservation of marine ecosystems. The traditional methods of mapping benthic habitats, which involve multibeam data acquisition and manually collecting and annotating imagery data, are time-consuming. However, with technological advances, using machine learning (ML) algorithms with structure-from-motion (SfM) photogrammetry has become a promising approach for mapping benthic habitats accurately and at very high resolutions. This paper explores using SfM photogrammetry and extreme gradient boosting (XGBoost) classifier for benthic habitat 3D mapping of a vertical wall at the Charlie-Gibbs Fracture Zone in the North Atlantic Ocean. The classification workflow started with extracting frames from video footage. The SfM was then applied to reconstruct the 3D point cloud of the wall. Thereafter, nine geometric features were derived from the 3D point cloud geometry. The XGBoost classifier was then used to classify the vertical wall into rock, sponges, and corals (Case 1 - three classes). In addition, we separated the sponges class into three types of sponges: Demospongiae, Hexactinellida, and other Porifera (Case 2 - five classes). Moreover, we compared the results from XGBoost with the widely used ML classifier, random forest (RF). For Case 2, XGBoost achieved an overall accuracy (OA) of 74.45%, while RF achieved 73.10%. The OA improved by about 10% from both classifiers when the three types of sponges were combined into one class (Case 1). Results showed that the presented 3D mapping of benthic habitat has the potential to provide more detailed and accurate information about marine ecosystems.

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Section: Related Workmentioning

confidence: 99%

“…These maps represent habitat types, and can be used to monitor changes over time, and inform management decisions. Traditionally, benthic habitat mapping has employed multibeam echosounder data acquisition (Brown et al, 2011;Trzcinska et al, 2020) and underwater imagery collection and annotation (Keogh et al, 2022;Mohamed et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

3d Mapping of Benthic Habitat Using Xgboost and Structure From Motion Photogrammetry

Morsy,

Yánez Suárez,

Robert

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…To solve the difficulties of massive image data processing, deep learning algorithms are proving to be a suitable solution [8] . Deep learning algorithms have been proposed as a powerful tool for monitoring different underwater habitats from recorded images or videos, including shallow and turbid waters [9] , or deep benthic communities [10] .…”

Section: Introductionmentioning

confidence: 99%

Density estimation of the main structuring sessile species in underwater marine caves with a deep learning approach

Sierra,

Prado,

Rodríguez-Cobo

et al. 2023

JGC

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Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

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“…However, these frameworks require large annotated datasets and computational requirements for producing meaningful results (Gilardi 1995;Yuval et al, 2021). In the context of deep-water environments, the lack of training data available has reportedly been the bottleneck for advances of ML algorithms (Roelfsema et al, 2021;Walker, Bennett, and Thornton 2021;Mohamed, Nadaoka, and Nakamura 2022). Although annotation frameworks to gather specific data from underwater environments have been developed (Beijbom et al, 2012;Beijbom et al, 2015;Zurowietz et al, 2018), there is still a need for specifically designed dataset benchmarks for the application of robust ML methods for 3D points clouds of deep-water environments.…”

Section: Introductionmentioning

confidence: 99%

High-resolution 3D mapping of cold-water coral reefs using machine learning

Oliveira

Lim

Conti

et al. 2022

Front. Environ. Sci.

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Structure-from-Motion (SfM) photogrammetry is a time and cost-effective method for high-resolution 3D mapping of cold-water corals (CWC) reefs and deep-water environments. The accurate classification and analysis of marine habitats in 3D provide valuable information for the development of management strategies for large areas at various spatial and temporal scales. Given the amount of data derived from SfM data sources such as Remotely-Operated Vehicles (ROV), there is an increasing need to advance towards automatic and semiautomatic classification approaches. However, the lack of training data, benchmark datasets for CWC environments and processing resources are a bottleneck for the development of classification frameworks. In this study, machine learning (ML) methods and SfM-derived 3D data were combined to develop a novel multiclass classification workflow for CWC reefs in deep-water environments. The Piddington Mound area, southwest of Ireland, was selected for 3D reconstruction from high-definition video data acquired with an ROV. Six ML algorithms, namely: Support Vector Machines, Random Forests, Gradient Boosting Trees, k-Nearest Neighbours, Logistic Regression and Multilayer Perceptron, were trained in two datasets of different sizes (1,000 samples and 10,000 samples) in order to evaluate accuracy variation between approaches in relation to the number of samples. The Piddington Mound was classified into four classes: live coral framework, dead coral framework, coral rubble and sediment and dropstones. Parameter optimisation was performed with grid search and cross-validation. Run times were measured to evaluate the trade-off between processing time and accuracy. In total, eighteen variations of ML algorithms were created and tested. The results show that four algorithms yielded f1-scores >90% and were able to discern between the four classes, especially those with usually similar characteristics, e.g., coral rubble and dead coral. The accuracy variation among them was 3.6% which suggests that they can be used interchangeably depending on the classification task. Furthermore, results on sample size variations show that certain algorithms benefit more from larger datasets whilst others showed discrete accuracy variations (<5%) when trained in datasets of different sizes.

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Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

Cited by 8 publications

References 67 publications

3d Mapping of Benthic Habitat Using Xgboost and Structure From Motion Photogrammetry

3d Mapping of Benthic Habitat Using Xgboost and Structure From Motion Photogrammetry

Density estimation of the main structuring sessile species in underwater marine caves with a deep learning approach

High-resolution 3D mapping of cold-water coral reefs using machine learning

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