Paper Info
Title
LP norm SAR tomography by iteratively reweighted least square: First results
Abstract
Synthetic aperture radar tomography (TomoSAR) estimates the scene reflectivity along range, azimuth and elevation directions. Even if many works in recent literature deal with this topic, TomoSAR imaging remains a not easy procedure. In this work, the possibility to improve quality of imaging by a priori information is investigated experimentally; in particular we focus on urban scenario where targets of interest are point-like and radiometrically strong. Accordingly, we look for a sparse reflectivity function; this can be obtained minimizing the solution in an arbitrary Lp norm using the It-eratively Reweighted Least Square (IRLS) algorithm. Based on an experimental comparison among different choices for p, the conclusion drawn is that the usual choice p = 1 is the best trade-off between resolution and robustness to noise. Therefore, L1 norm minimization by IRLS has been exploited to perform CS TomoSAR on real data, and we report in this paper first results obtained using COSMO-SKyMed data acquired over an area in Milan, Italy.
Year
DOI
Venue
2014
10.1109/IGARSS.2014.6946674
Geoscience and Remote Sensing Symposium
Keywords
DocType
ISSN
radar imaging,remote sensing by radar,synthetic aperture radar,COSMO-SKyMed data,IRLS algorithm,Italy,LP norm SAR tomography,Milan,TomoSAR imaging,imaging quality,iteratively reweighted least square,sparse reflectivity function,synthetic aperture radar,Iteratively Reweighted Least Square (IRLS),Urban tomography,sparse representation,synthetic aperture radar (SAR)
Conference
2153-6996
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Simone Mancon142.52
Stefano Tebaldini233844.90
Andrea Monti Guarnieri321039.80