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  4. Adaptive sidelobe reduction in SAR and INSAR COSMO-SkyMed image processing
 
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Adaptive sidelobe reduction in SAR and INSAR COSMO-SkyMed image processing

Author(s)
Lorusso, R.
Lombardi, N.
Milillo, Giovanni  
Date Issued
2016
Publisher
SPIE
Abstract
The main lobe and the side lobes of strong scatterers are sometimes clearly visible in SAR images. Sidelobe reduction is of particular importance when imaging scenes contain objects such as ships and buildings having very large radar cross sections. Amplitude weighting is usually used to suppress sidelobes of the images at the expense of broadening of mainlobe, loss of resolution and degradation of SAR images. The Spatial Variant Apodization (SVA) is an Adaptive SideLobe Reduction (ASLR) technique that provides high effective suppression of sidelobes without broadening mainlobe. In this paper, we apply SVA to process COSMO-SkyMed (CSK) StripMap and Spotlight X-band data and compare the images with the standard products obtained via Hamming window processing. Different test sites have been selected in Italy, Argentina, California and Germany where corner reflectors are installed. Experimental results show clearly the resolution improvement (20%) while sidelobe kept to a low level when SVA processing is applied compared with Hamming windowing one. Then SVA technique is applied to Interferometric SAR image processing (INSAR) using a CSK StripMap interferometric tandem-like data pair acquired on East-California. The interferometric coherence of image pair obtained without sidelobe reduction (SCS-U) and with sidelobe reduction performed via Hamming window and via SVA are compared. High resolution interferometric products have been obtained with small variation of mean coherence when using ASLR products with respect to hamming windowed and no windowed one. © 2016 SPIE..
URI
https://hdl.handle.net/20.500.13025/5880
Journal
Proceedings of SPIE
DOI
10.1117/12.2241194
URL
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011416498&doi=10.1117%2f12.2241194&partnerID=40&md5=6e22844e1079ae152d87009bc5ff0b12
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