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Abstract
This paper deals with detection from multipolarization SAR images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the Polarimetric Covariance Matrix (PCM) equality (absence of target in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the back scattering associated with the target leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is apriori known the absence of targets. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant False Alarm Rate (CFAR) property. At the analysis stage, the behaviour of the new architecture is investigated in comparison with a benchmark (but non-implementable) and some other adaptive sub-optimum detectors available in open literature. The study, conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach.
Original language | English |
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Title of host publication | 2016 Sensor Signal Processing for Defence (SSPD) |
Place of Publication | Piscataway, NJ. |
Publisher | IEEE |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781509003266 |
DOIs | |
Publication status | Published - 18 Oct 2016 |
Event | 6th Conference of the Sensor Signal Processing for Defence - Royal College of Surgeons, Edinburgh, United Kingdom Duration: 22 Sep 2016 → 23 Sep 2016 Conference number: 6 |
Conference
Conference | 6th Conference of the Sensor Signal Processing for Defence |
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Abbreviated title | SSPD 2016 |
Country | United Kingdom |
City | Edinburgh |
Period | 22/09/16 → 23/09/16 |
Keywords
- constant false alarm rate
- covariance matrix equality
- multi-family generalized likelihood ratio test
- polarimetric SAR images
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Projects
- 1 Finished
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Signal Processing Solutions For the Networked Battlespace
Clemente, C. & Soraghan, J.
1/04/13 → 31/03/18
Project: Research