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Hobley, Brandon, Arosio, Riccardo, French, Geoffrey, Bremner, Julie, Dolphin, Tony and Mackiewicz, Michal 
ORCID: https://orcid.org/0000-0002-8777-8880
(2021)
Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery.
Remote Sensing, 13 (9).
ISSN 2072-4292
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Abstract
Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in the environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improves the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared—Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps. FCNNs are an emerging set of algorithms within Deep Learning. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiresolution segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with the standard OBIA method used by ecologists.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being |
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Colour and Imaging Lab Faculty of Science > Research Groups > Collaborative Centre for Sustainable Use of the Seas |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 22 May 2021 00:08 |
| Last Modified: | 20 Jun 2024 00:46 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/80073 |
| DOI: | 10.3390/rs13091741 |
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