This work is focused on the interpretation of multispectral images of oil spills, by introducing an optical model of
spectral signature for oil-covered sea surface. Oil spill detection and oil type identification can potentially be achieved
using data from multispectral optical sensors. However, multispectral images interpretation is challenging, because the
spectral signature depends not only on oil optical properties and film thickness, but also on the optical properties of the
water column, the incident light distribution and the instrument viewing geometry. In this work a simulator has been
developed, starting from an optical model for both clean and polluted surfaces, which makes it possible to analyze
variability in the optical signal from an oil-covered water surface. Several simulations have been performed varying the
water optical properties within a range typical of Case I waters, and considering different crude and refined oils. Incident
light distributions and viewing configurations have been chosen according to a typical viewing geometry of the MERIS
sensor over a particularly interesting Mediterranean area: the marine ecosystem of the Tuscan Archipelago. The results,
shown in terms of both upwelling radiance and oil-water optical contrast, provide some general rules that may aid
interpretation of MERIS data. In particular, the detectability of an oil slick has been shown to depend on oil type and
film thickness: very thin oil films (sheens) are more easily detected at viewing directions near the sun-glint zone, while
very thick films are more likely to be detected at viewing angles away from the sun. For films of intermediate thickness
the detectability depends mainly on the oil's specific optical properties.
This work presents a comparative experimental analysis of different Anomaly Detectors (ADs) carried out on a high
spatial resolution data set acquired by the prototype hyperspectral sensor SIM-GA. The benchmark AD for hyperspectral
anomaly detection is the Reed-Xiaoli (RX) algorithm. Its main limitation is the assumption that the local background can
be modeled by a Gaussian distribution. In the literature, several ADs have been presented, most of them trying to cope
with the problem of non-Gaussian background. Despite the variety of works carried out on such algorithms, it is difficult
to find a comparative analysis of these methodologies performed on the same data set and therefore in identical operating
conditions. In this work, the most known ADs, such as the RX, Orthogonal Subspace Projection (OSP) based algorithms,
the Cluster Based AD (CBAD), and the Signal Subspace Processing AD (SSPAD) are analyzed and compared,
highlighting their most interesting characteristics. The performance is evaluated on a new data set relative to a rural
scenario, in which several man-made targets have been embedded. The non-homogeneous nature of the background,
enhanced by the high spatial resolution of the sensor, and the presence of man-made artifacts, like buildings and
vehicles, make the anomaly detection process very challenging. Performance comparison is carried out on the basis of a
joint analysis of the Receiving Operative Characteristics and the image statistics. For this data set, the best performance
is obtained by the strong background suppression ability of the OSP-based algorithm.
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