Next Article in Journal
Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration
Next Article in Special Issue
Geometric, Environmental and Hardware Error Sources of a Ground-Based Interferometric Real-Aperture FMCW Radar System
Previous Article in Journal
Climate Data Records from Meteosat First Generation Part I: Simulation of Accurate Top-of-Atmosphere Spectral Radiance over Pseudo-Invariant Calibration Sites for the Retrieval of the In-Flight Visible Spectral Response
Previous Article in Special Issue
Cross-Pol Transponder with Frequency Shifter for Bistatic Ground-Based Synthetic Aperture Radar
Open AccessArticle

Compressive Sensing for Ground Based Synthetic Aperture Radar

Department of Information Engineering, University of Florence, via Santa Marta, 3, 50139 Firenze, Italy
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1960;
Received: 10 October 2018 / Revised: 30 November 2018 / Accepted: 2 December 2018 / Published: 5 December 2018
Compressive sensing (CS) is a recent technique that promises to dramatically speed up the radar acquisition. Previous works have already tested CS for ground-based synthetic aperture radar (GBSAR) performing preliminary simulations or carrying out measurements in controlled environments. The aim of this article is a systematic study on the effective applicability of CS for GBSAR with data acquired in real scenarios: an urban environment (a seven-storey building), an open-pit mine, and a natural slope (a glacier in the Italian Alps). The authors tested the most popular sets of orthogonal functions (the so-called ‘basis’) and three different recovery methods (l1-minimization, l2-minimization, orthogonal pursuit matching). They found that Haar wavelets as orthogonal basis is a reasonable choice in most scenarios. Furthermore, they found that, for any tested basis and recovery method, the quality of images is very poor with less than 30% of data. They also found that the peak signal–noise ratio (PSNR) of the recovered images increases linearly of 2.4 dB for each 10% increase of data. View Full-Text
Keywords: compressive sensing; ground based synthetic aperture radar; radar; synthetic aperture radar compressive sensing; ground based synthetic aperture radar; radar; synthetic aperture radar
Show Figures

Graphical abstract

MDPI and ACS Style

Pieraccini, M.; Rojhani, N.; Miccinesi, L. Compressive Sensing for Ground Based Synthetic Aperture Radar. Remote Sens. 2018, 10, 1960.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop