Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept

dc.contributor.advisorCoetzee, Serena Martha
dc.contributor.emailbarend.vandermerwe@up.ac.zaen_US
dc.contributor.postgraduateVan der Merwe, Barend Jacobus
dc.date.accessioned2023-02-06T08:40:35Z
dc.date.available2023-02-06T08:40:35Z
dc.date.created2023
dc.date.issued2022
dc.descriptionThesis (PhD (Geography))--University of Pretoria, 2022.en_US
dc.description.abstractRemotely sensed imagery is a valuable source of data for studying barchan morphology. However, manual methods of data extraction constrain both the spatial and temporal resolution of studies because they are time consuming to carry out. Therefore, to effectively use the increasing availability of remotely sensed imagery, novel methods need to be developed that can extract the desired data from imagery automatically. Convolutional Neural Networks (CNNs) have shown promise in identifying landforms from imagery, but its suitability for barchan research remains untested. Since CNNs are strongly influenced by the texture of the image, it can be questioned whether the classification is based on the image’s texture (which can vary due to solar angles and atmospheric disturbances) or the geometry of the landform. Additionally, deviations in shape and other morphometric properties can manifest as subtle alterations to the barchan’s geometry. This poses a challenge for CNNs which have difficulty in distinguishing between similarly shaped landforms. Using a small sample of dunes from the Kunene region in Namibia, it is shown that CNNs can: distinguish between different morphologic classes of barchans in the absence of image texture with accuracies exceeding 80%, and distinguish between similarly shaped landfroms. When used along with methods of barchan outline extraction, a suitably trained CNN can automatically extract barchan morphologic data from remotely sensed imagery. This can increase both the temporal and spatial resolution of barchan research.en_US
dc.description.availabilityUnrestricteden_US
dc.description.degreePhD (Geography)en_US
dc.description.departmentGeography, Geoinformatics and Meteorologyen_US
dc.identifier.citation*en_US
dc.identifier.doi10.25403/UPresearchdata.21959837en_US
dc.identifier.otherA2023en_US
dc.identifier.urihttps://repository.up.ac.za/handle/2263/89156
dc.language.isoenen_US
dc.publisherUniversity of Pretoria
dc.rights© 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
dc.subjectUCTDen_US
dc.subjectConvolutional neural networksen_US
dc.subjectBarchansen_US
dc.subjectVGG16en_US
dc.subjectResNet50en_US
dc.subjectLandformsen_US
dc.titleClassifying barchan outlines into morphological classes using convolutional neural networks : a proof of concepten_US
dc.typeThesisen_US

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