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This paper deals with detection of oil spills from multi-polarization 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 oil spills 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 oil spills leads to a signal, in some eigen-directions, weaker than the one gathered from a reference area where it is a-priori known the absence of any oil slicks. A Multi-family Generalized Likelihood Ratio Test (MGLRT) approach is pursued to come up with an adaptive detector ensuring the Constant Alarm False Rate (CFAR) property. At the analysis stage, the behavior of the new architecture is investigated in comparison with a benchmark (but non-implementable) structure and some other sub-optimum adaptive 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.
|Number of pages||17|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Early online date||18 Oct 2016|
|Publication status||Published - 31 Jan 2017|
- covariance matrix equality
- oil spills detection
- one-sided GLRT
- multi-family generalized likelihood ratio test
- constant alarm false rate
- polarimetric covariance matrix
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- 1 Finished
Clemente, C. & Soraghan, J.
1/04/13 → 31/03/18