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Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model
Thesis (MSc)--Stellenbosch University, 2015.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2015
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| _version_ | 1867613783082401792 |
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| access_status_str | Open Access |
| author | Etoughe Kongo, Ulrich Pavlique |
| author2 | Poona, Nitesh |
| author_browse | Etoughe Kongo, Ulrich Pavlique Poona, Nitesh |
| author_facet | Poona, Nitesh Etoughe Kongo, Ulrich Pavlique |
| author_sort | Etoughe Kongo, Ulrich Pavlique |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--Stellenbosch University, 2015. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/96914 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:41:37.777Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/96914 Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model Etoughe Kongo, Ulrich Pavlique Poona, Nitesh Van Niekerk, Adriaan Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies. GeoEye-1 imagery LiDar UCTD Image processing Land use, Urban -- South Africa -- Cape Town Land use, Urban -- South Africa -- Cape Town -- Classification Land use mapping -- South Africa -- Cape Town Land use, Urban -- South Africa -- Cape Town -- Remote sensing Digital surface model Thesis (MSc)--Stellenbosch University, 2015. ENGLISH ABSTRACT: Urban planning and management require up-to-date information about urban land cover. Producing such geospatial information is time consuming as it is usually done manually. The classification of such information from satellite imagery is challenging owing to the difficulties associated with distinguishing urban features having similar spectral properties. Therefore, this study evaluates the combination of a digital surface model (DSM) derived from LiDAR data and very high-resolution GeoEye-1 satellite imagery for classifying urban land cover in Cape Town. The value of the DSM was assessed by comparing a land cover product obtained from the GeoEye-1 image to a map produced using both the GeoEye-1 image and the DSM. A systematic segmentation procedure for the two classifications scenarios preceded a supervised (using a support vector machine, K nearest neighbour and classification and regression algorithm tree classifiers) and rule-based classification. The various approaches were evaluated using a combination of methods. When including the DSM in the supervised and rule-based classifications, the overall accuracy and kappa vary between 80% to 83% and 0.74 to 0.77 respectively. When the DSM is excluded, the overall accuracy ranges between 49 to 64% whereas kappa ranges between 0.32 to 0.53 for the two classification approaches. The accuracies obtained are always about 20% higher when the DSM is included. The normalised DSM (nDSM) enabled accurate discrimination of elevated (e.g. buildings) and non-elevated (e.g. paved surfaces) urban features having similar spectral characteristics. The nDSM of at least one-metre resolution and one metre vertical accuracy influenced the accuracy of the results by correctly differentiating elevated from non-elevated. The rule-based approach was more effective than the supervised classification, particularly for extracting water bodies (dams and swimming pools) and bridges. Consequently, a rule-based approach using very high spatial resolution (EHSR) satellite imagery and a LiDAR-derived DSM is recommended for mapping urban land cover. AFRIKAANSE OPSOMMING: Stedelike beplanning- en bestuur vereis dat inligting oor grondbedekking (land cover) op datum moet wees. Die vervaardiging van hierdie georuimtelike inligting is tydrowend omdat dit gewoonlik met die hand gedoen word. Die onttrekking van sulke inligting vanuit satellietbeelde bied ʼn groot uitdaging omdat stedelike voorwerpe met soortgelyke spektrale eienskappe moeilik is om van mekaar te onderskei. Hierdie studie evalueer die kombinasie van ʼn digitale oppervlak model (DOM) afkomstig van LiDAR-data en ʼn baie hoë resolusie GeoEye-1-satellietbeeld om stedelike grondbedekking in Kaapstad te klassifiseer. Die waarde van die DOM word bepaal deur ʼn grondbesettingsproduk wat vanuit ʼn GeoEye-1-beeld verkry is te vergelyk met ʼn grondbesettingsproduk wat verkry is deur beide die GeoEye-1-beeld en die DOM te gebruik. Sistematiese segmentasie word op die twee benaderings uitgeoefen en dit word gevolg deur ʼn gekontroleerde klassifikasie (steunvektormasjiene, k-naaste aangrensende waarde en klassifikasie en regressie algoritme) en ʼn reël-gebaseerde algoritme. Hierdie verskeie benaderings is geëvalueer met behulp van ʼn kombinasie van kwalitatiewe en kwantitatiewe metodes. Toe die DOM in die gekontroleerde en reël-gebaseerde klassifikasie ingesluit is, het die algehele akkuraatheid en kappa tussen 80% en 83%, en 74% en 77% gewissel. Toe die DOM uitgesluit is, het die algehele akkuraatheid en kappa tussen 49% en 64%, en 32% en 53% vir die twee klassifikasiebenaderings gewissel. Die behaalde akkurraatheidswaardes is altyd 20% hoër as die DOM ingesluit word. Dit is hoofsaaklik omdat die DOM akkurate onderskeiding tussen hoë (bv. geboue) en plat (bv. geplaveide oppervlaktes) stedelike bakens met gelyksoortige spektrale eienskappe in staat stel. Die kwaliteit van die DOM beïnvloed die akkuraatheid van die resultate. ʼn DOM van ten minste een meter resolusie, met een meter of beter vertikale akkuraatheid, word benodig om te verseker dat geboue en ander beboude bakens korrek van mekaar onderskei kan word. Die reël-gebaseerde benadering was meer effektief as die gekontroleerde klassifikasie, veral om waterliggame (damme en swembaddens) en brûe te identifiseer. Gevolglik word ʼn reël-gebaseerde benadering met die hoë resolusie satellietbeelde en ʼn LiDAR-afgeleide DOM aanbeveel om stedelike grondbesetting te karteer. Masters 2015-05-20T09:28:22Z 2015-05-20T09:28:22Z 2015-04 Thesis http://hdl.handle.net/10019.1/96914 en_ZA Stellenbosch University 105 pages : illustrations, maps application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | GeoEye-1 imagery LiDar UCTD Image processing Land use, Urban -- South Africa -- Cape Town Land use, Urban -- South Africa -- Cape Town -- Classification Land use mapping -- South Africa -- Cape Town Land use, Urban -- South Africa -- Cape Town -- Remote sensing Digital surface model Etoughe Kongo, Ulrich Pavlique Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model |
| title | Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model |
| title_full | Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model |
| title_fullStr | Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model |
| title_full_unstemmed | Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model |
| title_short | Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model |
| title_sort | urban land cover classification from high resolution geoeye 1 imagery using a lidarbased digital surface model |
| topic | GeoEye-1 imagery LiDar UCTD Image processing Land use, Urban -- South Africa -- Cape Town Land use, Urban -- South Africa -- Cape Town -- Classification Land use mapping -- South Africa -- Cape Town Land use, Urban -- South Africa -- Cape Town -- Remote sensing Digital surface model |
| url | http://hdl.handle.net/10019.1/96914 |
| work_keys_str_mv | AT etoughekongoulrichpavlique urbanlandcoverclassificationfromhighresolutiongeoeye1imageryusingalidarbaseddigitalsurfacemodel |