Abstract
In this paper, we investigate the performance of partition features derived from histogram analysis to isolate dark spots which are candidates to be oil spills in SAR images. The first partition is carried out to obtain preliminary clusters of the pixels on the basis of their grey level intensities and threshold values deduced from the histogram. The detection process is achieved by a contextual partition where the conflict pixels are attributed to their region involving local information about pre-etiqueted pixels neighbouring the pixel in question. For pixel’s assignment, we propose two decision criteria: the first based on Local Probability Maximization (LPM) while the second uses a Chi-squared test (χ 2). We considered variable context in order to characterize the sea texture and dark spots. This method is tested on ERS-2 SAR Precision Image (PRI) covering Algerian coasts and gave promising results which are useful for the identification process.
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Lounis, B., Belhadj-Aissa, A. Sea SAR Images Analysis to Detect Oil Slicks in Algerian Coasts. J Math Model Algor 13, 371–386 (2014). https://doi.org/10.1007/s10852-014-9250-3
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DOI: https://doi.org/10.1007/s10852-014-9250-3