{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:06:59Z","timestamp":1760242019321,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,5]],"date-time":"2018-12-05T00:00:00Z","timestamp":1543968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 \u2018basis\u2019) 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\u2013noise ratio (PSNR) of the recovered images increases linearly of 2.4 dB for each 10% increase of data.<\/jats:p>","DOI":"10.3390\/rs10121960","type":"journal-article","created":{"date-parts":[[2018,12,5]],"date-time":"2018-12-05T12:22:00Z","timestamp":1544012520000},"page":"1960","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Compressive Sensing for Ground Based Synthetic Aperture Radar"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3661-726X","authenticated-orcid":false,"given":"Massimiliano","family":"Pieraccini","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, via Santa Marta, 3, 50139 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4948-6270","authenticated-orcid":false,"given":"Neda","family":"Rojhani","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, via Santa Marta, 3, 50139 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-4588","authenticated-orcid":false,"given":"Lapo","family":"Miccinesi","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, via Santa Marta, 3, 50139 Firenze, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1080\/01431160600554975","article-title":"Advances in groundbased microwave interferometry for landslide survey: A case study","volume":"27","author":"Luzi","year":"2006","journal-title":"Int. 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