Extraction of Buildings in Urban Area for Surface Area Assessment from Satellite Imagery based on Morphological Building
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RESEARCH ARTICLE
Extraction of Buildings in Urban Area for Surface Area Assessment from Satellite Imagery based on Morphological Building Index using SVM Classifier R. Avudaiammal1
•
P. Elaveni1 • Shirley Selvan1 • Vijayarajan Rajangam2
Received: 16 August 2019 / Accepted: 26 August 2020 Indian Society of Remote Sensing 2020
Abstract 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. Keywords Building extraction MBI Edge detection Otsu NDVI SVM
Introduction The application domains of remote sensing have been significantly augmented with accessibility of sub-meter resolution data from high-resolution earth satellites, such as IKONOS, WorldView-2 and QuickBird (Liu et al. 2008, Gavankar et al. 2018). In order to distinguish natural
& R. Avudaiammal [email protected] P. Elaveni [email protected] Shirley Selvan [email protected] Vijayarajan Rajangam [email protected] 1
Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering, Chennai, India
2
SENSE, VIT, Chennai, India
objects (vegetation, bare land, mountains, water bodies) and man-made objects (buildings, track, road) with more accuracy, researchers started working on Very High Resolution (VHR) images captured by these satellites. Rooftops are essential features to be extracted from satellite images due to their significance in applications such as urban cadastral management, 3D map reconstruction, updation of urban geodatabase, risk assessment and rescue map (You et al. 2018). Nowadays, the urban sector is rapidly growing and consequently more energy is being consumed. Due to depletion of the ozone layer, the weather becomes unpredictable, days become longer, and summer becomes hotter requiring more ventilation a
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