Building Footprint Extraction from LiDAR Data Using SVM Classification

Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing. In this work presents a novel approach to detect the buildings by atomization of the training area collecting stage for supervised classification. The classification method for the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI). The method employs the multi-spectral remote sensing data technique to find spectral signature of different objects such as vegetation index, land cover classification, concrete structure, road structure, rural and urban areas, rocky areas and remaining areas presented in the image. In the experiments, the proposed SVM framework outperformed for the three (Linear SVM, Cubic SVM, Medium Gaussian SVM) methods tested. SVM is trained through supervised learning to classify each location in the image as “buildings” or “Non-buildings.” Finally, SVM algorithm to find the accuracy value, training time and receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods.