CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES
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. Init...
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| Language: | en |
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2023
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| Online Access: | http://eprints.uthm.edu.my/10566/1/J16422_5d34d2c1a46d457f2048e15c371f1607.pdf http://eprints.uthm.edu.my/10566/ |
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| author | Asmala Ahmad, Asmala Ahmad Mohd Yazid Abu Sari, Mohd Yazid Abu Sari Hamzah Sakidin, Hamzah Sakidin Suliadi Firdaus Sufahani, Suliadi Firdaus Sufahani Abd Rahman Mat Amin, Abd Rahman Mat Amin Abd Wahid Rasib, Abd Wahid Rasib |
| author_facet | Asmala Ahmad, Asmala Ahmad Mohd Yazid Abu Sari, Mohd Yazid Abu Sari Hamzah Sakidin, Hamzah Sakidin Suliadi Firdaus Sufahani, Suliadi Firdaus Sufahani Abd Rahman Mat Amin, Abd Rahman Mat Amin Abd Wahid Rasib, Abd Wahid Rasib |
| author_sort | Asmala Ahmad, Asmala Ahmad |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-10566 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2023 |
| record_format | eprints |
| spelling | my.uthm.eprints-105662024-01-03T01:37:16Z http://eprints.uthm.edu.my/10566/ CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES Asmala Ahmad, Asmala Ahmad Mohd Yazid Abu Sari, Mohd Yazid Abu Sari Hamzah Sakidin, Hamzah Sakidin Suliadi Firdaus Sufahani, Suliadi Firdaus Sufahani Abd Rahman Mat Amin, Abd Rahman Mat Amin Abd Wahid Rasib, Abd Wahid Rasib T Technology (General) 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. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10566/1/J16422_5d34d2c1a46d457f2048e15c371f1607.pdf Asmala Ahmad, Asmala Ahmad and Mohd Yazid Abu Sari, Mohd Yazid Abu Sari and Hamzah Sakidin, Hamzah Sakidin and Suliadi Firdaus Sufahani, Suliadi Firdaus Sufahani and Abd Rahman Mat Amin, Abd Rahman Mat Amin and Abd Wahid Rasib, Abd Wahid Rasib (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 |
| spellingShingle | T Technology (General) Asmala Ahmad, Asmala Ahmad Mohd Yazid Abu Sari, Mohd Yazid Abu Sari Hamzah Sakidin, Hamzah Sakidin Suliadi Firdaus Sufahani, Suliadi Firdaus Sufahani Abd Rahman Mat Amin, Abd Rahman Mat Amin Abd Wahid Rasib, Abd Wahid Rasib CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES |
| title | CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES |
| title_full | CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES |
| title_fullStr | CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES |
| title_full_unstemmed | CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES |
| title_short | CLASSIFICATION OF OBJECTS WITHIN AGRICULTURAL LANDSCAPE FROM HIGH-RESOLUTION AERIAL IMAGERY USING MAXIMUM LIKELIHOOD DISCRIMINANT RULES |
| title_sort | classification of objects within agricultural landscape from high-resolution aerial imagery using maximum likelihood discriminant rules |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10566/1/J16422_5d34d2c1a46d457f2048e15c371f1607.pdf http://eprints.uthm.edu.my/10566/ |
| url_provider | http://eprints.uthm.edu.my/ |