Abstract
This paper deals with detection of oil spills from multipolarization synthetic aperture radar 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 backscattering associated with the oil spills leads to a signal, in some eigendirections, weaker than the one gathered from a reference area where the absence of any oil slicks is a priori known. A multifamily generalized likelihood ratio test approach is pursued to come up with an adaptive detector ensuring the constant false alarm rate property. At the analysis stage, the behavior of the new architecture is investigated in comparison with a benchmark (but nonimplementable) structure and some other suboptimum adaptive detectors available in the open literature. This study, which is conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach.
Highlights
O IL spills represent a threat to the environment, wildlife, and to human life through the food chain
For the sake of completeness, we provide in the following the explicit expression of ζGi (ZK, Y M ), which contains the constants which have been incorporated into the threshold of the PDD-generalized likelihood ratio test (GLRT), i.e., ζGi (ZK, Y M ) = 0 if p∗ < p and ζGi(ZK, Y M ) = − 2(K + M )r(i) log(K + M ) + 2Kr(i) log(K) + 2M r(i) log(M )
The maximum-likelihood detector (MLD) proposed in [8], which is given by det(H ) H1
Summary
O IL spills represent a threat to the environment, wildlife, and to human life through the food chain. Oil pipeline breakages, and illegal human activities are the causes of the presence of oil spills in the sea. Illegal washing activities of oil tankers contribute 70% to sea pollution with oil [1]. As this kind of activity is difficult to prevent, monitoring and detection of the phenomenon is vital to be able to act timely and avoid natural disasters. Oil spills have peculiar characteristics that make them visible in synthetic aperture radar (SAR) images. Oil smooths the sea surface, reducing its roughness, appearing darker in SAR images
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