Automatic Extraction of Buildings from UAV-Based Imagery Using Artificial Neural Networks
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RESEARCH ARTICLE
Automatic Extraction of Buildings from UAV-Based Imagery Using Artificial Neural Networks Prakash Pilinja Subrahmanya1
•
Bharath Haridas Aithal1 • Satarupa Mitra2
Received: 6 October 2020 / Accepted: 22 October 2020 Ó Indian Society of Remote Sensing 2020
Abstract The rapid growth of major cities across the counties demands accurate building extraction techniques. Unmanned Aerial Vehicles (UAV) help in obtaining terrain information that can be used to extract urban features. Recent advancement has led to the capture of aerial images of the earth surface in micro-detail using UAV. These aerial images enable us to perform classification, feature extraction, and height estimation at a finer scale. In this work, aerial images of the university campus were captured using a quadcopter drone equipped with high-resolution camera and satellite navigation system. Approximately 500 images were captured in the study area with necessary overlap and side lap. Captured images were subjected to aerial triangulation, dense image matching, and point cloud generation to produce Digital Surface Models (DSM) and orthophoto. Various machine learning algorithms—random forest (RF), support vector machine (SVM), naı¨ve Bayes (NB) and artificial neural networks (ANN)—have been used to extract building rooftops from derivatives of UAV-captured imageries, and accuracies were compared. Algorithms were trained using both spectral and elevation information to extract building rooftops, and improvements shown due to the addition of elevation data in training the model are observed. The proposed method is aimed at improving building-level information extraction and providing accurate building information to aid authorities for better planning and management. Keywords Artificial neural network Building extraction Machine learning SVM RF UAV
Introduction Urbanization is a complex and irreversible phenomenon that is basically due to the migration of people to cities in pursuit of employment, education facilities, and development of housing, industries, transportation infrastructure, etc. (Bharath et al. 2017). Urban areas constitute the majority of paved surfaces representing complex nature corresponding to varied surface types and heights in a remotely sensed image. Rapid redevelopment activities in major cities across the counties demand accurate building extraction techniques. Although space-based sensors are & Prakash Pilinja Subrahmanya [email protected]; [email protected] 1
Ranbir and Chitra Gupta School of Infrastructure Design and Management, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
2
Symbiosis Institute of Geoinformatics, Symbiosis Institute of Technology, Pune, Maharashtra 412115, India
capable of proving high-resolution imagery, UAV (drone)captured imagery gives the sophistication of cloud-free and timely data. Satellite-based position-enabled drones provide the advantage of generating accurate DSM from remotely sensed images. Drone-captured images are us
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