Spaceborne SAR image formation enhancement using MOCO techniques

Synthetic aperture radar (SAR) is considered a prevailing tool for remote sensing. It benefits working with high efficiency in all weather and all-day circumstances, making SAR is very confident compared to other types of remote sensing. The SAR platform moves with constant velocity and height, with...

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Bibliographic Details
Published in:	The Egyptian journal of remote sensing and space sciences Vol. 25; no. 3; pp. 659 - 671
Main Authors:	Fouad, Mohamed, Elbohy, Ahmed, Mashaly, Ahmed, Abosekeen, Ashraf, Abdalla, Ahmed, Azouz, Ahmed
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2022 Elsevier
Subjects:	algorithms Chirp scaling algorithm (CSA) entropy Low earth orbit (LEO) Motion compensation (MOCO) satellites synthetic aperture radar Synthetic aperture radar (SAR) weather Low earth orbit (LEO) Motion compensation (MOCO) Chirp scaling algorithm (CSA) Synthetic aperture radar (SAR)
ISSN:	1110-9823
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Summary:	Synthetic aperture radar (SAR) is considered a prevailing tool for remote sensing. It benefits working with high efficiency in all weather and all-day circumstances, making SAR is very confident compared to other types of remote sensing. The SAR platform moves with constant velocity and height, with a linear path for ideal situations. However, this assumption is not realized in satellite movement, which is an elliptical orbiting that worsens the quality of the focused image. This paper introduces a methodology of motion compensation for motion errors due to satellite elliptical orbiting and perturbations in an orbital path. It represents two major contributions applied on a low earth orbit (LEO) spaceborne SAR. First, motion errors analysis in the range and azimuth directions. Second, an algorithm for motion error compensation (MOCO) combined with a chirp scaling algorithm (CSA) is performed. Moreover, a validation for the formulated algorithm is executed using sentinel-1 level-0 real raw data input, and the result is compared with the sentinel-1 level-1 single look complex (SLC) SAR image. The validation is performed using two different metrics. First, image quality measurement by sharpness, contrast, and entropy. Second, measuring the peak-sidelobe-ratio (PSLR), impulse-response-width (IRW), and integrated-sidelobe-ratio (ISLR) for five high power reflecting points in the scene area.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1110-9823
DOI:	10.1016/j.ejrs.2022.06.001