Woollard, Michael;
Blacknell, David;
Griffiths, Hugh;
Ritchie, Matthew;
(2022)
SARCASTIC v2.0 - High-performance SAR simulation for next-generation ATR systems.
MDPI Remote Sensing
, 14
(11)
p. 2561.
10.3390/rs14112561.
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Abstract
Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes.
Type: | Article |
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Title: | SARCASTIC v2.0 - High-performance SAR simulation for next-generation ATR systems |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.3390/rs14112561 |
Publisher version: | https://doi.org/10.3390/rs14112561 |
Language: | English |
Additional information: | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited |
Keywords: | synthetic aperture radar; simulation; raytracing; automatic target recognition |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10150719 |
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