Extraction of Buildings in Urban Area for Surface Area Assessment from Satellite Imagery based on Morphological Building Index using SVM Classifier

Rooftops are essential features, extracted from satellite images for their significance in applications such as update of urban geodatabase, risk assessment and rescue map. In this work, a methodology (MBION-SVM) which integrates morphological, spectral, shape and geometrical features with SVM classifier to classify the objects within the satellite image into building rooftops and non-rooftops has been proposed. The probable buildings are detected using Morphological Building Index (MBI). The mislabeled rooftops are eliminated by combining Otsu thresholding and Normalized Difference Vegetation Index (NDVI). Geometrical features computed from identified building rooftops are used to train a support vector machine (SVM), and self-correction is performed for removing any mislabeled rooftops and to provide the data on surface area of the perfect rooftops. Here, we have used Very High Resolution (VHR) images of Worldview-2 and Sentinal-2. We have analyzed the performance of the proposed building extraction approach with classification algorithms such as linear discriminant analysis, logistic regression and SVM. Since the proposed method gives an accuracy around 99%, precision of 89%, a perfect recall of 1 and a F-score of 88%, it can be effectively utilized to extract the buildings from VHR images for any appropriate application.