Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.creator | Hu, Chen | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/5974 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Efficient change detection using Principal Component Analysis-based approach | en_US |
| dcterms.abstract | Change detection techniques based on remote sensing data have been developed for several decades and provided important information for planning and decision-making. In practical applications, some analysts and researchers need a rough and quick detection to locate the change areas for a further detailed analysis. Thus, an efficiency method is needed to fulfill this requirement. This study describes an investigation into the efficiency and effectiveness of a methodology based on Principal Component Analysis (PCA). An experiment test has been conducted for the efficiency and effectiveness evaluation of this method. In this experiment, five pairs of multidate image samples with different sizes are performed by both PCA-based approach and post-classification comparison. It is found that PCA-based method can provide a significant improvement in efficiency. It is also noticed that PCA-based approach are slightly better in accuracy performance, which has proven its effectiveness in detecting changes. | en_US |
| dcterms.extent | vii, 59 leaves : ill. (some col.) ; 30 cm. | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2011 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Principal components analysis | en_US |
| dcterms.LCSH | Remote sensing | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b24122749.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.37 MB | Adobe PDF | View/Open |
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