Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.


A multi-family GLRT-based algorithm for oil spill detection

De Maio, Antonio and Orlando, Danilo and Pallotta, Luca and Clemente, Carmine (2017) A multi-family GLRT-based algorithm for oil spill detection. IEEE Transactions on Geoscience and Remote Sensing, 55 (1). pp. 63-79. ISSN 0196-2892

Text (De-Maio-IEEETGRS2016-multi-family-GLRT-based-algorithm-for-oil-spill-detection)
De_Maio_IEEETGRS2016_multi_family_GLRT_based_algorithm_for_oil_spill_detection.pdf - Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (2MB) | Preview


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.