Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Semi-supervised segmentation for coastal monitoring seagrass using RPA imagery

Version 1 : Received: 30 March 2021 / Approved: 31 March 2021 / Online: 31 March 2021 (15:53:19 CEST)

A peer-reviewed article of this Preprint also exists.

Hobley, B.; Arosio, R.; French, G.; Bremner, J.; Dolphin, T.; Mackiewicz, M. Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery. Remote Sens. 2021, 13, 1741. Hobley, B.; Arosio, R.; French, G.; Bremner, J.; Dolphin, T.; Mackiewicz, M. Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery. Remote Sens. 2021, 13, 1741.

Journal reference: Remote Sens. 2021, 13, 1741
DOI: 10.3390/rs13091741

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 environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve 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, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale 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 standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

Keywords

Deep learning; Computer vision; Remote sensing; Supervised learning; Semi-supervised learning; Segmentation; Seagrass mapping

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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