SARCASTIC v2.0—High-Performance SAR Simulation for Next-Generation ATR Systems

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 ham...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 11; p. 2561
Main Authors Woollard, Michael, Blacknell, David, Griffiths, Hugh, Ritchie, Matthew A.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2022
Subjects
Automatic target recognition
Datasets
Investigations
Machine learning
Object recognition
Phenomenology
raytracing
Remote sensing
Sensors
Simulation
Software
Synthetic aperture radar
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14112561