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Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept
Thesis (PhD (Geography))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613635936780288 |
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| access_status_str | Open Access |
| author2 | Coetzee, Serena Martha |
| author_browse | Coetzee, Serena Martha |
| author_facet | Coetzee, Serena Martha |
| collection | Thesis |
| dc_rights_str_mv | © 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. |
| description | Thesis (PhD (Geography))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/89156 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:39:17.410Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/89156 Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept Coetzee, Serena Martha barend.vandermerwe@up.ac.za Van der Merwe, Barend Jacobus UCTD Convolutional neural networks Barchans VGG16 ResNet50 Landforms Thesis (PhD (Geography))--University of Pretoria, 2022. Remotely 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. Geography, Geoinformatics and Meteorology PhD (Geography) Unrestricted 2023-02-06T08:40:35Z 2023-02-06T08:40:35Z 2023 2022 Thesis * A2023 https://repository.up.ac.za/handle/2263/89156 10.25403/UPresearchdata.21959837 en © 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. application/pdf University of Pretoria |
| spellingShingle | UCTD Convolutional neural networks Barchans VGG16 ResNet50 Landforms Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept |
| title | Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept |
| title_full | Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept |
| title_fullStr | Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept |
| title_full_unstemmed | Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept |
| title_short | Classifying barchan outlines into morphological classes using convolutional neural networks : a proof of concept |
| title_sort | classifying barchan outlines into morphological classes using convolutional neural networks a proof of concept |
| topic | UCTD Convolutional neural networks Barchans VGG16 ResNet50 Landforms |
| url | https://repository.up.ac.za/handle/2263/89156 |