Classification of objects within agricultural landscape from high-resolution aerial imagery using maximum likelihood discriminant rules

Ahmad, Asmala and Abu Sari, Mohd Yazid and Sakidin, Hamzah and Sufahani, Suliadi Firdaus and Mat Amin, Abd Rahman and Rasib, Abd Wahid (2023) Classification of objects within agricultural landscape from high-resolution aerial imagery using maximum likelihood discriminant rules. ARPN Journal Of Engineering And Applied Sciences, 18 (8). pp. 936-948. ISSN 1819-6608

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Abstract

In this study, the performance of object classification based on four discriminant functions, namely linear, quadratic, diagonal linear and diagonal quadratic is investigated and compared. High-resolution aerial imagery captured from a UAV-based remote sensing platform is used for this purpose. Initially, K-means clustering of 9 clusters is used to assist in the selection of training pixels for the subsequent supervised classification implementation. The classification is experimented with using a training set size of 10 through 100 pixels for each of the discriminant functions. The outcome of the classification shows that training set size 40 and 10 are to be the optimal training set sizes for linear and quadratic discriminant function, and diagonal linear and quadratic discriminant function respectively. Overall, the linear discriminant function is found to have the highest overall accuracy of 91% followed by diagonal linear, quadratic, and diagonal quadratic discriminant function with overall accuracies of 82%, 79%, and 73% respectively.

Item Type:	Article
Uncontrolled Keywords:	Classification, Accuracy, Training set size, Discriminant function, Paddy
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	04 Jul 2024 11:49
Last Modified:	04 Jul 2024 11:49
URI:	http://eprints.utem.edu.my/id/eprint/27548
Statistic Details:	View Download Statistic

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