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Information Journal Paper

Title

Building Extraction from Fused Hyperspectral and LiDAR Data using Machine Learning Technique

Pages

  1-14

Abstract

 In this study, the fusion of hyperspectral and LiDAR data was used to propose a new method to detect buildings using the machine learning algorithm. The data sets provided by the National Science Foundation (NSF)-funded by Centre for Airborne Laser Mapping (NCALM)-over the University of Houston 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 (accuracy and precision). After classification of the buildings, a comparison was made between the results obtained by the proposed method and the reference method in this field. Our proposed method showed a better accuracy for buildings detection in a much shorter time compared to the reference method. 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. 94 were obtained, respectively.

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  • Cite

    APA: Copy

    Sajjadi, Seyed Yousef, & Parsian, Saeid. (2018). Building Extraction from Fused Hyperspectral and LiDAR Data using Machine Learning Technique. REMOTE SENSING & GIS, 10(2 ), 1-14. SID. https://sid.ir/paper/360531/en

    Vancouver: Copy

    Sajjadi Seyed Yousef, Parsian Saeid. Building Extraction from Fused Hyperspectral and LiDAR Data using Machine Learning Technique. REMOTE SENSING & GIS[Internet]. 2018;10(2 ):1-14. Available from: https://sid.ir/paper/360531/en

    IEEE: Copy

    Seyed Yousef Sajjadi, and Saeid Parsian, “Building Extraction from Fused Hyperspectral and LiDAR Data using Machine Learning Technique,” REMOTE SENSING & GIS, vol. 10, no. 2 , pp. 1–14, 2018, [Online]. Available: https://sid.ir/paper/360531/en

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