|
|
|
Evaluation of conventional digital camera scenes for Thematic Information Extraction
Table 9: The accuracy of each class using Maximum Likelihood classification.
| Class |
Maximum Likelihood |
| Producer Accuracy (%) |
User Accuracy (%) |
| Grass |
88.060 |
90.769 |
| Water |
93.506 |
80.899 |
| Land |
50.000 |
81.481 |
| Urban |
50.000 |
31.579 |
Conclusion
In this study, Maximum Likelihood was the best classifier to extract thematic information
from remote sensed imagery. The high spatial resolution images gave a more detail
deposition mapping of the classified map. So it is good for a small coverage of study
area. From the result of the accuracy assessment, we were quite confident of the
classified shown. Digital camera imagery provides a cheaper way to acquired remote
sensed imagery for land cover mapping.
Acknowledgement
This project was carried out using the Malaysian Government IRPA grant no. 08-02-05-
6011 and USM short term grant FPP2001/130. We would like to thank the technical staff
and research officers who participated in this project. Thanks are extended USM for
support and encouragement.
Reference
- Bruzzone, L., Cossu, R. and Vernazza, G. (2002). Combining parametric and nonparametric
algorithms for a partially unsupervised classification of
multitemporal remote-sensing images. Information Fusion 3, 289 –297.
- Donoghue, D. N. M. and Mironnet, N. (2002). Development of an integrated
geographical information system prototype for coastal habitat monitoring.
Computers and Geosciences, 28, 129-141.
- Foody, G. M. 2002. Status of land cover classification accuracy assessment. Remote
Sensing and Environment, 80, 185-201.
- Koponen, S., Pulliainen, J., Kallio, K. and Hallikainen, M. (2002). Lake water quality
classification with airborne hyperspectral spectrometer and simulated MERIS
data. Remote Sensing of Environment 79, 51– 59.
- Microsoft Corp., Map of Kedah, Malaysia. (2001). [online].
http://worldtwitch.virtualave.net/kedah_map.htm
|
|
|