A multi-family GLRT for detection in polarimetric SAR images
Pallotta, L. and Clemente, C. and De Maio, A. and Orlando, D.; (2016) A multi-family GLRT for detection in polarimetric SAR images. In: 2016 Sensor Signal Processing for Defence (SSPD). IEEE, GBR, pp. 1-5. ISBN 9781509003266 (https://doi.org/10.1109/SSPD.2016.7590584)
Preview |
Text.
Filename: Pallotta_etal_SSPD_2016_A_multi_family_GLRT_for_detection.pdf
Accepted Author Manuscript Download (505kB)| Preview |
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.
ORCID iDs
Pallotta, L., Clemente, C. ORCID: https://orcid.org/0000-0002-6665-693X, De Maio, A. and Orlando, D.;-
-
Item type: Book Section ID code: 59967 Dates: DateEvent18 October 2016Published23 June 2016AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 24 Feb 2017 16:33 Last modified: 04 Dec 2024 01:06 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/59967