| Author: | Hu, Jinyuan |
| Title: | Development of high resolution satellite image classification method in land use and land cover mapping |
| Advisors: | Wong, Man Sing Charles (LSGI) |
| Degree: | M.Sc. |
| Year: | 2015 |
| Subject: | Remote-sensing images. Remote sensing -- Data processing. Image processing. Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Construction and Environment |
| Pages: | viii, 87 page : color illustrations |
| Language: | English |
| Abstract: | As the development of remote sensing technology in land use and land cover mapping, classification method of high resolution satellite image has become more and more important and popular in the application. As one of the most important technique of accuracy improvement, multiple classifier systems (MCS) not only attract increasing interest in the field of remote sensing image classification, but also become hot topics in pattern recognition and machine learning. In this thesis, Satellite Pour 1' Observation de la Terre (SPOT) VI multi-spectral satellite images are applied and classified into 8 land cover classes using four base classifiers, they are maximum likelihood classifier, neural network classifier, Mahalanobis distance classifier and minimum distance classifier. These four classifiers are trained using the same data sets. The prior probability and the a posteriori probability are derived from the confusion matrices of those classifiers, which are produce accuracy and user accuracy respectively. A multiple classifier system is designed in this thesis to combine these classifiers to improve the accuracy of the classification result. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b2831542x.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.9 MB | Adobe PDF | View/Open |
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