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The authors present a new method to characterise and discriminate oil slicks and some look-alikes in ERS-2 SAR images according only to the observed sea roughness, to reduce oil spill detection and monitoring systems cost. It exploits sea wave spectrum images from the multiscale analysis based on a modified morphological pyramid. Many backscatter characteristics extracted at each level, depended on object and background features are normalized to make its spectral scales be identical. Twenty objects (spot and border) backscatter features have been measured. Eleven sea surface slicks types have been analysed, namely oil, atmospheric instability, wind front, unstable air-mass, current front, falling land wind, large gravity waves, low wind area, natural slicks, swell visible and wind sheltered area. The results presented as smoothed basic profiles and textural spectra allow to tackle oil slicks supervised classification in new images. Oil slicks and current front are discriminated. But, some ambiguities of slicks discrimination in SAR images remain persistent.