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WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. Univ Rwanda, Coll Sci & Technol, Ctr Geog Informat Syst & Remote Sensing, KN 67 St,POB 3900, Nyarugenge, Kigali, Rwanda..ORCID iD: 0000-0002-6425-6035
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
2019 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 11, no 18, article id 2128Article in journal (Refereed) Published
Abstract [en]

The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user's accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 11, no 18, article id 2128
Keywords [en]
WorldView-2, high-resolution, object-based classification, Support Vector Machine, geometric ruleset, urban density index, greenness density index, urban land cover, informal settlements
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-262981DOI: 10.3390/rs11182128ISI: 000489101500058Scopus ID: 2-s2.0-85072634011OAI: oai:DiVA.org:kth-262981DiVA, id: diva2:1367029
Note

QC 20191031

Available from: 2019-10-31 Created: 2019-10-31 Last updated: 2019-10-31Bibliographically approved

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Mugiraneza, TheodomirNascetti, AndreaBan, Yifang

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