Multi-aspect target detection for SAR imagery using hidden Markov models

Paul Runkle, Lam H. Nguyen, James H. McClellan, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


Radar scattering from an illuminated object is often highly dependent on the target-sensor orientation. In typical synthetic aperture radar (SAR) imagery, the information in the multi-aspect target signatures is diffused in the image-formation process. In an effort to exploit the aspect dependence of the target signature, we employ a sequence of directional filters to the SAR imagery, thereby generating a sequence of subaperture images that recover the directional dependence of the target scattering. The scattering statistics are then used to design a hidden Markov model (HMM), wherein the orientation-dependent scattering statistics are exploited explicitly. This approach fuses information embodied in the orientation-dependent target signature under the assumption that both the target identity and orientation are unknown. Performance is assessed by considering the detection of tactical targets concealed in foliage, using measured foliage-penetrating (FOPEN) SAR data.
Original languageEnglish (US)
Pages (from-to)46-55
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number1
StatePublished - Jan 1 2001
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2021-02-09


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