The increased amount of available Synthetic Aperture Radar (SAR) images involves a growing workload on the
operators at analysis centers. In addition, even if the operators go through extensive training to learn manual
oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements
of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are
of great benefit. In this paper we present the potentialities of TerraSAR-X (TS-X) data and Neural Network
algorithms for oil spills detection. The radar on board satellite TS-X provides X-band images with a resolution
of up to 1m. Such resolution can be very effective in the monitoring of coastal areas to prevent sea oil pollution.
The network input is a vector containing the values of a set of features characterizing an oil spill candidate.
The network output gives the probability for the candidate to be a real oil spill. Candidates with a probability
less than 50% are classified as look-alikes. The overall classification performances have been evaluated on a
data set of 50 TS-X images containing more than 150 examples of certified oil spills and well-known look-alikes
(e.g. low wind areas, wind shadows, biogenic films). The preliminary classification results are satisfactory
with an overall detection accuracy above 80%.
The properties of single look complex SAR satellite images have already been analyzed by many investigators. A
common belief is that, apart from inverse SAR methods or polarimetric applications, no information can be gained from
the phase of each pixel. This belief is based on the assumption that we obtain uniformly distributed random phases when
a sufficient number of small-scale scatterers are mixed in each image pixel. However, the random phase assumption does
no longer hold for typical high resolution urban remote sensing scenes, when a limited number of prominent human-made
scatterers with near-regular shape and sub-meter size lead to correlated phase patterns. If the pixel size shrinks to a
critical threshold of about 1 meter, the reflectance of built-up urban scenes becomes dominated by typical metal
reflectors, corner-like structures, and multiple scattering. The resulting phases are hard to model, but one can try to
classify a scene based on the phase characteristics of neighboring image pixels. We provide a "cooking recipe" of how to
analyze existing phase patterns that extend over neighboring pixels.
High Resolution (HR) Synthetic Aperture Radar (SAR) Single Look Complex (SLC) observations, mainly of
strong scattering scenes or objects show phase patterns.
Phase patterns may occur due to the system behavior or they may be signatures of the imaged objects. Since
state of the art stochastic models of SAR SLC data describe mainly the pixel information. Now studies are
needed to elaborate better models for the full information content. Thus, new statistical models of HR SAR
SLC are proposed, they aim at the characterization of the spatial phase feature of Polarimetric SAR (PolSAR)
SLC data, i.e. they describe multi-band, complex valued textures.
The definition of texture must be changed because it is not anymore characterizing the optical features but
the electromagnetic properties of the illuminated targets.
The content of the SAR image is a stochastic process characterized from its own structure and geometry, which
differs from the real one of the illuminated scene, and is dominated from strong scatterers.
Nevertheless we are going to accept the classical texture definition, inherited from computer vision, in homogeneous
areas and, furthermore, we are going to extend it for a characterization of isolated and structured objects
The proposed models are in the class of simultaneous Auto-Regressive (sAR) defined on a generalized set of
cliques in the pixel vicinity.
Models may have different orders, thus capturing different degrees of the data complexity. To cope with the
problem of estimation and model order selection Bayesian inference is used.
The results are presented on PolSAR data.
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