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Sequential and non-sequential hypertemporal classification and change detection of Modis time-series
Thesis (PhD(Eng))--University of Pretoria, 2012.
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University of Pretoria
2013
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| _version_ | 1867613458898354176 |
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| access_status_str | Open Access |
| author2 | Olivier, Jan Corne |
| author_browse | Olivier, Jan Corne |
| author_facet | Olivier, Jan Corne |
| collection | Thesis |
| dc_rights_str_mv | © 2012 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(Eng))--University of Pretoria, 2012. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/25427 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:36:28.597Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| 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:2263/25427 Sequential and non-sequential hypertemporal classification and change detection of Modis time-series Olivier, Jan Corne Kleynhans, Waldo Van Zyl, A.J. trienkog@gmail.com Grobler, Trienko Lups Hypertemporal classification Inductive simulator Sequential analysis Hypertemporal change detection Support vector machine Cumulative sum Noise-harmonic features Coloured simple harmonic oscillator Moderate resolution imaging spectroradiometer Ornstein-uhlenbeck process UCTD Thesis (PhD(Eng))--University of Pretoria, 2012. Satellites provide humanity with data to infer properties of the earth that were impossible a century ago. Humanity can now easily monitor the amount of ice found on the polar caps, the size of forests and deserts, the earth’s atmosphere, the seasonal variation on land and in the oceans and the surface temperature of the earth. In this thesis, new hypertemporal techniques are proposed for the settlement detection problem in South Africa. The hypertemporal techniques are applied to study areas in the Gauteng and Limpopo provinces of South Africa. To be more specific, new sequential (windowless) and non-sequential hypertemporal techniques are implemented. The time-series employed by the new hypertemporal techniques are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which is on board the earth observations satellites Aqua and Terra. One MODIS dataset is constructed for each province. A Support Vector Machine (SVM) [1] that uses a novel noise-harmonic feature set is implemented to detect existing human settlements. The noise-harmonic feature set is a non-sequential hypertemporal feature set and is constructed by using the Coloured Simple Harmonic Oscillator (CSHO) [2]. The CSHO consists of a Simple Harmonic Oscillator (SHO) [3], which is superimposed on the Ornstein-Uhlenbeck process [4]. The noise-harmonic feature set is an extension of the classic harmonic feature set [5]. The classic harmonic feature set consists of a mean and a seasonal component. For the case studies in this thesis, it is observed that the noise-harmonic feature set not only extends the harmonic feature set, but also improves on its classification capability. The Cumulative Sum (CUSUM) algorithm was developed by Page in 1954 [6]. In its original form it is a sequential (windowless) hypertemporal change detection technique. Windowed versions of the algorithm have been applied in a remote sensing context. In this thesis CUSUM is used in its original form to detect settlement expansion in South Africa and is benchmarked against the classic band differencing change detection approach of Lunetta et al., which was developed in 2006 [7]. In the case of the Gauteng study area, the CUSUM algorithm outperformed the band differencing technique. The exact opposite behaviour was seen in the case of the Limpopo dataset. Sequential hypertemporal techniques are data-intensive and an inductive MODIS simulator was therefore also developed (to augment datasets). The proposed simulator is also based on the CSHO. Two case studies showed that the proposed inductive simulator accurately replicates the temporal dynamics and spectral dependencies found in MODIS data. Electrical, Electronic and Computer Engineering unrestricted 2013-09-06T21:17:24Z 2013-06-27 2013-09-06T21:17:24Z 2013-04-15 2012 2013-06-10 Thesis Grobler, TL 2012, Sequential and non-sequential hypertemporal classification and change detection of Modis time-series, PhD thesis, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/25427 > D13/4/707/ag http://hdl.handle.net/2263/25427 http://upetd.up.ac.za/thesis/available/etd-06102013-125210/ © 2012 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 application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf University of Pretoria |
| spellingShingle | Hypertemporal classification Inductive simulator Sequential analysis Hypertemporal change detection Support vector machine Cumulative sum Noise-harmonic features Coloured simple harmonic oscillator Moderate resolution imaging spectroradiometer Ornstein-uhlenbeck process UCTD Sequential and non-sequential hypertemporal classification and change detection of Modis time-series |
| title | Sequential and non-sequential hypertemporal classification and change detection of Modis time-series |
| title_full | Sequential and non-sequential hypertemporal classification and change detection of Modis time-series |
| title_fullStr | Sequential and non-sequential hypertemporal classification and change detection of Modis time-series |
| title_full_unstemmed | Sequential and non-sequential hypertemporal classification and change detection of Modis time-series |
| title_short | Sequential and non-sequential hypertemporal classification and change detection of Modis time-series |
| title_sort | sequential and non sequential hypertemporal classification and change detection of modis time series |
| topic | Hypertemporal classification Inductive simulator Sequential analysis Hypertemporal change detection Support vector machine Cumulative sum Noise-harmonic features Coloured simple harmonic oscillator Moderate resolution imaging spectroradiometer Ornstein-uhlenbeck process UCTD |
| url | http://hdl.handle.net/2263/25427 http://upetd.up.ac.za/thesis/available/etd-06102013-125210/ |