Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.creator | Abd-Elrahman, Ahmed Mohamed Shaker | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/2730 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Point- and line-based transformation models for high resolution satellite image rectification | en_US |
| dcterms.abstract | Rigorous mathematical models with the aid of satellite ephemeris data can present the relationship between the satellite image space and the object space. With government funded satellites, access to calibration and ephemeris data has allowed the development and use of these models. However, for commercial high-resolution satellites, which have been recently launched, these data are withheld from users, and therefore alternative empirical models should be used. In general, the existing empirical models are based on the use of control points and involve linking points in the image space and the corresponding points in the object space. But the lack of control points in some remote areas and the questionable accuracy of the identified discrete conjugate points provide a catalyst for the development of algorithms based on features other than control points. This research, concerned with image rectification and 3D geo-positioning determination using High-Resolution Satellite Imagery (HRSI), has two major objectives. First, the effects of satellite sensor characteristics, number of ground control points (GCPs), and terrain elevation variations on the performance of several point based empirical models are studied. Second, a new mathematical model, using only linear features as control features, or linear features with a minimum number of GCPs, is developed. To meet the first objective, several experiments for different satellites such as Ikonos, QuickBird, and IRS-1D have been conducted using different point based empirical models. Various data sets covering different terrain types are presented and results from representative sets of the experiments are shown and analyzed. The results demonstrate the effectiveness and the superiority of these models under certain conditions. From the results obtained, several alternatives to circumvent the effects of the satellite sensor characteristics, the number of GCPs, and the terrain elevation variations are introduced. To meet the second objective, a new model named the Line Based Transformation Model (LBTM) is developed for HRSI rectification. The model has the flexibility to either solely use linear features or use linear features and a number of control points to define the image transformation parameters. Unlike point features, which must be explicitly defined, linear features have the advantage that they can be implicitly defined by any segment along the line. The underlying principle of the developed model is that the relationship between unit vector components of line segments on linear features in the image space and the object space can be expressed as affine or conformal relationships. Similar to other empirical models, the LBTM does not require any sensor calibration or satellite ephemeris data. Synthetic as well as real data have been used to check the validity and reliability of the new model, and the results show that the LBTM performs to a level comparable with existing point based empirical models. | en_US |
| dcterms.extent | xv, 239 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2004 | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Remote-sensing images | en_US |
| dcterms.LCSH | Artificial satellites in remote sensing | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b17726888.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 7.76 MB | Adobe PDF | View/Open |
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