Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Construction and Environment | en_US |
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Zhu, Xiaolin (LSGI) | - |
| dc.creator | Zhou, Qiong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/10561 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Comparing three algorithms for interpolating missing pixels in Landsat imagery | en_US |
| dcterms.abstract | The data missing caused by the failure of the Enhanced Thematic Mapper (ETM+) sensor scan-line corrector (SLC) of Landsat 7 and cloud/cloud shadow obscuration severely hinder Landsat images applications. Therefore, the contaminated pixels must be removed and filled with data predicted to reconstruct the complete and continuous Landsat images. There have been a lot of methods developed for interpolating contaminated pixels caused by SLC gaps or cloud and cloud shadow. Three typical algorithms are tested in this study: (1) local linear histogram matching (LLHM); (2) neighborhood similar pixel interpolator (NSPI); (3) the harmonic model. Four experiments were designed to compare their performance under different conditions to explore their advantages and limits. The different influence factors in the prediction accuracy were discussed, including the regular gaps or agglomerate cloud/cloud shadow, the extent of the heterogeneity, the amount of temporally available information and the amount of spatially available information. All prediction results from the three methods were quantitatively assessed with correlation coefficient (R) and root mean square error (RMSE) between the actual values and predicted values of the contaminated pixels. In general, all methods generated reasonable reconstruction results. The harmonic model can have the super performance when the available data temporally is enough, of good quality and able to represent the seasonality of data. The NSPI is relatively robust under different conditions, which benefits by the combination of the prediction one (based on spatial information) and the prediction two (based on temporal information). The LLHM is simplest in principle. | en_US |
| dcterms.extent | 67 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2018 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Virtual reality | en_US |
| dcterms.LCSH | Computer simulation | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Tours | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022385341703411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.7 MB | Adobe PDF | View/Open |
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