Different unsupervised Bayesian classification algorithms can be associated to a multiscale image analysis procedure leading to improvements , both in computation time and classification performances. Two kinds of algorithms are used for the classification itself:-local methods on a pixel-by-pixel basis.-global methods , which require a Markov random field model for the whole class image. Unsupervised Bayesian classification requires two steps, one for the parameter estimation of each local or global model and one for the Bayesian classification itself. A Gaussian density with parameters depending on the class is assumed for the pixels. In a multiscale analysis scheme , the image is decomposed by successive filtering and downsampling , which allows to separate homogeneous areas and edges according to a pyramidal structure .One scale pyramid containing smaller and smaller smoothed images and one wavelet pyramid with the complementary information concerning details are built. Unsupervised Bayesian classification is done at each level of the scale pyramid , from top to bottom , by taking into account pixels which are assumed well classified at the previous level .The wavelet pyramid can be used to help the classification by defining if a classified pixel belongs to an homogeneous area or not. The homogeneity criterion consists in a variance comparison at each stage and a thresholding. A comparison has been made on very noisy synthetic images , which permits to measure the improvements and drawbacks brought by the multiscale analysis in local and global classification.
Ecole Nationale Sup6rieure des T616communications de Bretagne Groupe Traitement d h a g e s BP Abstract Unsupervised Bayesian segmentation applied to the whole radar image gives good results only when the look number is sufficiently high to approximate the intensity image Gamma probability density by a Gaussian law. This is not the case for real remote sensing radar images as ERSl where the look number is four. Multiscale image analysis by wavelets has proved to be efficient for increasing classification performances in the Gaussian case or for optical images[ 1 ].It is proposed to extend the method to radar images with low look numbers. In unsupervised Bayesian classification , the knowledge of the class
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