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Title: | Multi-modality and multi-sensor image registration for satellite images |
Authors: | Abela, Aaron (2021) |
Keywords: | Remote-sensing images Image registration Geographical location codes |
Issue Date: | 2021 |
Citation: | Abela, A. (2021). Multi-modality and multi-sensor image registration for satellite images (Master's dissertation). |
Abstract: | Satellite imagery provides information which is fundamental to remote sensing applications. Two of such applications are image registration and fusion of hyperspectral and multispectral imagery. Image registration is a fundamental pre-processing step to image fusion. Conjunctively, in remote sensing little previous work aimed at the registration of satellite imagery with significant scale differences and the registration of multi-modal satellite images. The aim of this work was to do a comprehensive analysis of registration techniques in remote sensing. The SIFT algorithm with different parameter sets was utilised to register thermal-thermal satellite imagery with significant scale differences. The work also examined and compared the use of other feature-based, area-based and optical flow-based techniques for the registration of multi-modal and multi-sensorial satellite imagery. The multi-modal data includes optical satellite imagery from Sentinel-2 and Landsat-8. SAR images from Sentinel-1 and thermal images from Landsat-8 and Sentinel-3. The findings of this study show that the most common type of modality utilised in the image registration of remote sensing data is Optical-Optical and synthetic aperure radar (SAR)-Optical. For the registration of thermal Landsat-8 to Landsat-8 and thermal Landsat-8 to Sentinel-3, the general pattern was that as one upscaled the sensed image, the misregistration and RMSE increased due to a higher scale difference. For the registration of SAR-Optical satellite imagery the overall best performing was SIFT-Flow. For the registration of single modality data, the overall best was SIFT followed by the Enhanced Correlation Coefficient (ECC). For the registration of multi-modal satellite imagery, the overall best was SIFT. |
Description: | M.Sc.(Melit.) |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/119234 |
Appears in Collections: | Dissertations - FacICT - 2021 Dissertations - FacICTCCE - 2021 |
Files in This Item:
File | Description | Size | Format | |
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2119ICTCCE591200000011_1.PDF | 68.75 MB | Adobe PDF | View/Open |
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