@article{scholars15607,
          volume = {12},
       publisher = {Science and Information Organization},
             doi = {10.14569/IJACSA.2021.0121120},
           title = {Na{\~A}?ve Bayes Classification of High-Resolution Aerial Imagery},
           pages = {168--177},
            note = {cited By 5},
         journal = {International Journal of Advanced Computer Science and Applications},
          number = {11},
            year = {2021},
          author = {Ahmad, A. and Sakidin, H. and Sari, M. Y. A. and Amin, A. R. M. and Sufahani, S. F. and Rasib, A. W.},
        keywords = {Aerial photography; Antennas; Classifiers; Discriminant analysis; Image classification; K-means clustering; Remote sensing, Classification accuracy; High resolution aerial imagery; K-means; Kappa coefficient; Linear discriminant analyze; Naive bayes; Naive Bayes classification; Quadratic discriminant analysis; Training set size; Training sets, Pixels},
        abstract = {In this study, the performance of Na{\~A}?ve 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 Na{\~A}?ve Bayes classification. The Na{\~A}?ve 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. {\^A}{\copyright} 2021. All Rights Reserved.},
            issn = {2158107X},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121279212&doi=10.14569\%2fIJACSA.2021.0121120&partnerID=40&md5=459f5f74d8577d128971fc5a8b3fa7bd}
}