Knowledge Resource Center for Ecological Environment in Arid Area
| Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model | |
| Etoughe Kongo;Ulrich Pavlique | |
| 出版年 | 2015 |
| 学位授予单位 | Stellenbosch : Stellenbosch University |
| 英文摘要 | 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 akkuraathe |
| 英文关键词 | GeoEye-1 imagery LiDar UCTD Image processing Digital surface model |
| URL | http://hdl.handle.net/10019.1/96914 |
| 来源机构 | Stellenbosch University |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/249119 |
| 推荐引用方式 GB/T 7714 | Etoughe Kongo;Ulrich Pavlique. Urban land cover classification from high resolution Geoeye-1 imagery using a lidarbased digital surface model[D]. Stellenbosch : Stellenbosch University,2015. |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| [Etoughe Kongo;Ulrich Pavlique]的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| [Etoughe Kongo;Ulrich Pavlique]的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| [Etoughe Kongo;Ulrich Pavlique]的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
