| Author | Bahani, Pervin Ong |
| Call Number | AIT Thesis no. CS-94-41 |
| Subject(s) | Remote sensing
|
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | In this study, a technique for land cover classification was proposed and implemented. The
proposed method uses a combination of supervised and unsupervised classification. Split and
merge clustering is applied to the selected training data and the output clusters will be used in the
assignment of the unknown pixel of the image to be classified. Two existing conventional methods
namely, maximum likelihood classification method (MLC) and minimum distance classification
method (MDC) were also implemented. The first method is based on statistical probabilities, the
second method uses distance as a discriminant to classify an unknown input pixel. Test sites were
selected and the performances of the three implemented classification methods were compared.
The test sites were classified into four types. The classification results showed that the proposed
method can give better classification accuracy, however classification time is longer for the
proposed classification method compared to both MLC and MDC. |
| Year | 1994 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Other Field of Studies (No Department) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Murai, Shunji
|
| Examination Committee(s) | Vilas Wuwongse ;Ochi, Shiro
|
| Scholarship Donor(s) | The Government of Japan |
| Degree | Thesis (M.Sc.) - Asian Institute of Technology, 1994 |