Mapping of Urban Features of Nnewi Metropolis Using High Resolution Satellite Image and Support Vector Machine Classifier

Innocent Chukwukalo Ezeomedo, Joel Izuchukwu Igbokwe


When maps and master plan of an area are available, they will definitely guide in the urban development, especially as a working document for enforcing planning laws by both government and the private urban developers. However, such basic geospatial information is reasonably lacking in this study area and sequel to this, the researcher aims at Mapping of Urban Features (Natural and Man-Made), which includes the Vegetation, wetlands/water bodies, buildings/pavement, open/bare surfaces and farm lands captured in GeoEye-1, High Resolution Satellite Image (HRSI) of 2016 using Support Vector Machine Classifier (SVMC) with a view of developing a reliable urban land use and land cover map of the area, which will serve as a base map for land-use planning and monitoring for a variety of end-users. The objectives include: to identify and extract features/regions of interest (ROIs) in a subset HRSI of the study area, to perform supervised classification using SVM in ENVI Software. The methodology used include Image acquisition, Image enhancement, Image Sub-setting, Image masking, Extraction of Regions of Interests (ROIs) and its separability index analysis, supervised classification using SVMC, Post-processing Accuracy Assessment, and Preparation of maps. Environment for Visualizing Image (ENVI 5.1) incorporated with Interactive Data Language (IDL 8.3) software was used for image processing, masking, spatial data analysis and image classification. Meanwhile, Esri ArcGIS 10.2 was employed for database development and production of thematic maps. Microsoft Excel and word was used for statistical analysis and result presentation. The result of image classification using SVMC, Radial Basis Function (BRF) default kernel in ENVI 5.1 indicates that Nnewi-North L.G.A is having 13.52% of Built-up Areas, 24.23% of Vegetation, 22.05% of Water bodies, Farm lands is equal to 39.40% and open/bare surface is 0.81% and result of image classification was validated using Error Matrix and Kappa Coefficient which results revealed that (SVMC overall Accuracy =98.07% and Kappa Coefficient = 0.97. The result revealed that ‘Support Vector Machine Classifier’ is robust in extracting urban landscape from HRSI, especially Built-up areas and open/bare for every Remote Sensing Analysis. The research recommends that it is imperative to check for ‘ROIs index separability’ before using it for classification, also there is need for periodic urban LULC analysis to guide stakeholders in Planning, Monitoring, and Management of Urban Areas..

Keywords: Support Vector Machine Classifier, Extraction of Regions of Interests (ROIs), ROIs separability index analysis, High Resolution Satellite Image (HRSI), Urban Landuse and Landcover.

DOI: 10.7176/JEES/9-6-11

Publication date:June 30th 2019

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ISSN (Paper)2224-3216 ISSN (Online)2225-0948

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