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Use of polarimetric features for oil spill characterization is relatively new and have not been used for operational services until now. In the last decade, a number of semi-automatic and automatic techniques have been proposed in order to differentiate oil spill and look-alike spots based on single pol SAR data, however these techniques suffer from a high miss-classification rate which is undesirable for operational services. In addition to that, small operational spillages from offshore platforms are often ignored as it appears insignificant on traditional ScanSAR (wide) images. In order to mitigate this situation a major focus of research in this area is the development of automated algorithms based on polarimetric images to distinguish oil spills from look-alikes. This paper describes the development of an automated Near Real Time (NRT) oil spill detection processing chain based on quad-pol RADARSAT-2 and quad-pol TerraSAR-X images using polarimetric features (e.g. Lexicographic and Pauli Based features). Number TerraSAR-X images acquired over known offshore platforms with same day ascending and descending configuration along with near coincident RADARSAT-2 acquisition. A total number of 10 polarimetric feature parameters were extracted from different types of oil (e.g. crude oil, emulsion etc) and look-alike spots and divided into training and validation dataset. Extracted features were then used for training and validation of a pixel based Artificial Neural Network (ANN) classifier. Initial performance estimation was carried out for the proposed methodology in order to evaluate its suitability for NRT operational service. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look- alike. Polarimetric features such as Scattering diversity, Surface scattering fraction, Entropy and Span proved to be more discriminative than other polarimetric features.
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