Naïve Bayes Classification of High-Resolution Aerial Imagery

• 主要な著者: Ahmad, A., Sakidin, H., Sari, M.Y.A., Amin, A.R.M., Sufahani, S.F., Rasib, A.W.
• フォーマット: 論文
• 出版事項: Science and Information Organization 2021
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121279212&doi=10.14569%2fIJACSA.2021.0121120&partnerID=40&md5=459f5f74d8577d128971fc5a8b3fa7bd; http://eprints.utp.edu.my/30349/

In this study, the performance of Naïve Bayes classification on a high-resolution aerial image captured from a UAV-based remote sensing platform is investigated. K-means clustering of the study area is initially performed to assist in selecting the training pixels for the Naïve Bayes classification. The Naïve Bayes classification is performed using linear and quadratic discriminant analyses and by making use of training set sizes that are varied from 10 through 100 pixels. The results show that the 20 training set size gives the highest overall classification accuracy and Kappa coefficient for both discriminant analysis types. The linear discriminant analysis with 94.44 overall classification accuracy and 0.9395 Kappa coefficient is found higher than the quadratic discriminant analysis with 88.89 overall classification accuracy and 0.875 Kappa coefficient. Further investigations carried out on the producer accuracy and area size of individual classes show that the linear discriminant analysis produces a more realistic classification compared to the quadratic discriminant analysis particularly due to limited homogenous training pixels of certain objects. © 2021. All Rights Reserved.