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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Asian Research Publishing Network (ARPN)
2023
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/27548/2/0064805092023349.PDF http://eprints.utem.edu.my/id/eprint/27548/ https://www.arpnjournals.org/jeas/research_papers/rp_2023/jeas_0423_9179.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|