| Autor: |
Man Wai Yip, A. Alexander G. Webb, Pablo J. González |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
International Journal of Applied Earth Observations and Geoinformation, Vol 125, Iss , Pp 103548- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1569-8432 |
| DOI: |
10.1016/j.jag.2023.103548 |
| Popis: |
Satellite radar imaging has been used as a remote sensing tool for studying Earth’s surface. High spatial resolution achieved by Synthetic Aperture Radar (SAR) allows to classify landcover and, using radar interferometry, to measure topography and surface deformation at millimeter scale. However, the handling of voluminous SAR data has presented a significant challenge over the past decade, leading to a technological barrier for researchers to study regional and global scale problems. A possible solution will be to explore data compression algorithms. Despite of the importance of SAR data compression, limited research has focused on downsizing user-level SAR images, and none of the existing studies have explicitly explored the impact of compression on interferometric (InSAR) processing.This study investigates compression algorithms to downsize Sentinel-1 single-look complex (SLC) images by a factor of 2–4 within signal noise levels. The performance of the compressed images was evaluated in the context of PS-InSAR processing, specifically on persistent scatterer (PS) candidate selection and displacement time-series estimation. We observe that even when images were compressed by a factor of 4, more than 98 % of the PS candidates were identical to those selected from the original uncompressed images. In terms of linear velocity, the discrepancy is |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect