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Synthetic Aperture Radar (SAR) images are an important source of information. This kind of imaging is little affected by adverse atmospheric conditions, such as ram, clouds, fog, etc., since it operates at frequencies other than the visible. Also, since the sensor is active and carries its own source of illumination, it can operate by night. The problem that arises with the use of this technology is a signal- dependent noise, called speckle. This kind of noise is common to all imaging devices that use coherent illumination, such as laser, microwaves, etc. One of the most useful techniques for image analysis is the segmentation. Using statistical modelling, two multiclass segmentation techniques for 1-look and linear detection SAR images are derived: the maximum likelihood and the Iterated Conditional Modes (ICM), both assuming multiplicative Rayleigh models for the data. Although the ICM segmentation yields significatively better results than the maximum likelihood segmentation, the 1-look linear detection case is noisy enough to deserve some improvement. Mathematical Morphology, a non linear approach to signal processing, is then used as a refinement technique in order to extract information.

<title>Improvement of 1-look SAR image segmentations with mathematical morphology</title>