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

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 201

Comparing the Capabilities of SVMC and MLC Using Contingency Matrix and a Novel Template

Since extraction of useful information from remote sensing data is important, scientists manage to propose efficient algorithms for automatic extraction of constructive information from the satellite imageries. To date, image classification has benefitted from advancements in improved computational power and algorithm development. Therefore, Satellite image classification using GeoEye-1, High Resolution Satellite Image (HRSI) of 2016, Support Vector Machine Classifier (SVMC) and Maximum Likelihood Classifier (MLC) were performed with a view to comparing the capabilities of SVMC and MLC using Post-processing Accuracy Assessment (PAA) and a Novel Template in producing urban land use and land cover map of the area. The objectives include performing supervised classification using SVM and MLC in ENVI Software, analysing the performance of SVM and MLC in mapping geometric features using error matrix and a new template. The methodology used comprise Image acquisition, Image enhancement, Image Sub-setting, Extraction of Regions of Interests (ROIs) and its separability index analysis, supervised classification using SVMC and MLC, Post-Processing Accuracy Assessment, Statistical Analyses, and Preparation of maps. ENVI 5.1 software was used for image processing, masking, spatial data analysis and classification. Microsoft Excel, GraphPad Prism ver.7.0 and IBM SPSS ver.21 were used for statistical analysis. The result of image classification 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% using SVMC while MLC result shows that Built-up Areas is14.99%, Vegetation is 13.01%, Water bodies is 34.08%, Farm lands is 36.00% and open/bare surface is 1.32%. Error Matrix and Kappa Coefficient results revealed that SVMC is better than MLC as follows (SVMC overall Accuracy is 98.07% and Kappa Coefficient is 0.97 while MLC overall Accuracy is 82.50% and Kappa Coefficient is 0.76. Additional statistical testing with aggregate mean from SVM and MLC was used to determine the significance of the mean difference using the researcher’s developed template called “Post Confusion Matrix” (PoCoMa). The result showed that the t-statistics is 0.670 with probability value of -0.476 which is greater than 0.05, thus, the null hypothesis was accepted with a deduction that using any of the algorithms (SVM and MLC) yields no significant difference in performance and efficiency of result of the map produced. The overall study revealed that both classifiers are efficient and accurate statistically, without any significant difference but using error matrix analysis, the research revealed that ‘Support Vector Machine Classifier’ is robust in extracting urban landscape from HRSI, especially Built-up areas and open/bare surfaces. The research recommends there is need for periodic urban LULC analysis to guide stakeholders in Planning, Monitoring, and Management of ‘Urban Areas’ among others. Keywords: Support Vector Machine Classifier (SVMC), Maximum Likelihood Classifier (MLC), Post Confusion Matrix (PoCoMa), High Resolution Satellite Image (HRSI), ENVI 5.1 software, and GraphPad Prism ver.7.0. DOI: 10.7176/JIEA/9-4-06 Publication date:June 30th 201