Seyed Yousef Sajjadi; Saeid Parsian
Volume 10, Issue 2 , September 2018, , Pages 1-14
Abstract
In this study, the fusion of hyperspectral and LiDAR data was used to propose a new method to detectbuildings using the machine learning algorithm. The data sets provided by the National ScienceFoundation (NSF) - funded by Centre for Airborne Laser Mapping (NCALM)- over the University ofHouston campus ...
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In this study, the fusion of hyperspectral and LiDAR data was used to propose a new method to detectbuildings using the machine learning algorithm. The data sets provided by the National ScienceFoundation (NSF) - funded by Centre for Airborne Laser Mapping (NCALM)- over the University ofHouston campus and the neighboring urban area, were used. The objectives of this study were: 1)automatic buildings extracting using the hyperspectral and LiDAR fused data (automation), 2)detecting of the maximum number of listed buildings on the study area (completeness), and 3)achieving the high accuracy in building detection throughout the classification procedure (accuracyand precision). After classification of the buildings, a comparison was made between the resultsobtained by the proposed method and the reference method in this field. Our proposed methodshowed a better accuracy for buildings detection in a much shorter time compared to the referencemethod. The accuracy of the classification was assessed by four parameters of Precision,Completeness, Overall Accuracy and Kappa Coefficient, and the values of 96%, 100%, 99% and 0.94were obtained, respectively.
