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MULTISCALE SEGMENTATION OF OIL SLICK IN SAR IMAGES BASED ON MORPHOLOGICAL PYRAMID

ABSTRACT We propose a new approach based on the Marangoni theory, particularly a dampening of the wave spectra energy. The observation is decomposed into multiscale analysis using a pseudo morphological pyramid, named here as a alternating contextual filter, to improve the detection of local variations of the wave spectrum. The morphological thick gradient contrast is first performed with a varying structuring element. The filtering is balanced by the low pass image. The residue image gives information about the surface energy spectrum in relation to the dispersion equation. Then images generated are merged with the help of fuzzy c-mean algorithm to achieve the segmentation process. The method is tested on ERS2 SAR and ENVISAT ASAR Images. The obtained results are promising and show an improvement of the oil slick detection. 1. INTRODUCTION ERS Synthetic Aperture Radar (SAR) images had proved their interest in large scale detection and monitoring of pollution on ocean surface. Advanced SAR (ASAR) images greatly offers new possibilities of applications [1]. Up until now, several methods based on intensity images have been used to measure signatures of oil slick by a spaceborne mono-frequency Synthetic Aperture Radar (SAR) [2] [3]. Among these methods, the Multiscale Oil Slick Segmentation (MOSS) with Markov Chain Model (MCM) [4] has been based on the behaviour of wave spectra [5]. It has proved multiscale analysis to be an original approach to explain the effects of slick on sea, to characterize ocean surface polluted, particularly the dampening of the wave spectra energy, and then to classify oil slicks pixels. In this previous work, the multiscale analysis has been implemented by wavelet packets decomposition for frequential texture characterization. But, the local variation of the wave spectrum required being characterise structurally [3]. The multiscale contrast thick gradients resulting from the morphological pyramid [6], with a varying structuring element, are non linear approaches suitable to answer these two points of view. A conflict area (beyond the possible classes) has been appeared too in the images detection result of the MCM. To raise this ambiguity, the locations will be refining with fuzzy model algorithm. The paper is organized as follows. Section 2 recalls the main concept of ocean modelling. Then, the method (section 3) operates in two mains steps: the multiscale decomposition by morphological pyramid based on contextual filtering and the multi spectral fuzzy classification performed to merge additional sub-bands generated. Experimental results are illustrated in section 4, at the same time, on ENVISAT ASAR and ERS2 SAR images, to evaluate these two radar results. Conclusions are reported in section 5.