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Contextual methods for multisource land cover classification with application to Radarsat and SPOT data

For the classification of the radar data, several techniques have been developed, which take the statistical properties of the radar distribution into account and use a priori segmentation to have better contextual information. The introduction of synthetic neo-channels, describing the local texture of radar images, improve the classification process. We also test two different processes to minimize the inter- class confusion caused by the speckle noise: a pixel-by-pixel basis classification which requires a preliminary spatial and/or temporal speckle filtering, or a contextual method without filtering. In the case of the multi-source data classification, we present a fusion algorithm which consists in implementing different statistical rules for radar or optical images.