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Assessing classification performance for sampled remote sensing data
Mini Dissertation (MSc)--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_ | 1867613474860826624 |
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| access_status_str | Open Access |
| author2 | Fabris-Rotelli, Inger Nicolette |
| author_browse | Fabris-Rotelli, Inger Nicolette |
| author_facet | Fabris-Rotelli, Inger Nicolette |
| 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 | Mini Dissertation (MSc)--University of Pretoria 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/89449 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:43.949Z |
| 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:2263/89449 Assessing classification performance for sampled remote sensing data Fabris-Rotelli, Inger Nicolette u17052395@tuks.co.za Thiede, Renate Rangongo, Tshepiso Selaelo UCTD Sampling Remote sensing Crop classification Mini Dissertation (MSc)--University of Pretoria 2022. The volume of big data increases daily. Big data poses challenges in storage, management, processing, analysis and visualisation. One technique of handling big data is the use of subset or sample that is good representation of the data. For storage alleviation purposes, a subset of the big data can be obtained from metadata. This paper obtains metadata of a remote sensing image dataset for crop classification. This research proposes a sampling algorithm which makes use of multivariate stratification with the aim of obtaining a sample that best represents the population while minimising the number of images sampled. The proposed sampling algorithm performs effectively on a big spatial image dataset of crop types. The results are assessed by measuring the number of images sampled and as well as matching the proportionality of the population crop percentages. The samples obtained from the proposed algorithm are then used for land cover classification, these will be referred to as the proposed samples. An ensemble method called random forest is trained on the different samples and the accuracy is assessed. Precision, recall and F1-scores per crop type are computed as well as the overall accuracy. The random forest classifier performed best on the proposed sample with the least number of images, followed by the proposed sample with the second least number of images. The classifier performed better on the proposed samples than it did on the random samples as the proposed samples contained the most informative data. This research encourages the use of metadata for classification purposes as well as an effective way of sampling big data for crop classification. NEPTTP Statistics MSc Unrestricted 2023-02-13T13:05:17Z 2023-02-13T13:05:17Z 2023 2022 Mini Dissertation * A2023 https://repository.up.ac.za/handle/2263/89449 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 Sampling Remote sensing Crop classification Assessing classification performance for sampled remote sensing data |
| title | Assessing classification performance for sampled remote sensing data |
| title_full | Assessing classification performance for sampled remote sensing data |
| title_fullStr | Assessing classification performance for sampled remote sensing data |
| title_full_unstemmed | Assessing classification performance for sampled remote sensing data |
| title_short | Assessing classification performance for sampled remote sensing data |
| title_sort | assessing classification performance for sampled remote sensing data |
| topic | UCTD Sampling Remote sensing Crop classification |
| url | https://repository.up.ac.za/handle/2263/89449 |