人工智能
计算机科学
人工神经网络
深度学习
模式识别(心理学)
卷积神经网络
特征提取
计算机视觉
作者
Prakash Pilinja Subrahmanya,Bharath H. Aithal,Satarupa Mitra
标识
DOI:10.1007/s12524-020-01235-z
摘要
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), naive 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.
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