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

Danielle Ducrot; Hugues Sassier; Juste Mombo; Stephane Goze; Jean-Guy Planes

doi:10.1117/12.331867

4 December 1998 Contextual methods for multisource land cover classification with application to Radarsat and SPOT data

Danielle Ducrot, Hugues Sassier, Juste Mombo, Stephane Goze, Jean-Guy Planes

Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331867
Event: Remote Sensing, 1998, Barcelona, Spain

Abstract

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.

Citation Download Citation

Danielle Ducrot, Hugues Sassier, Juste Mombo, Stephane Goze, and Jean-Guy Planes "Contextual methods for multisource land cover classification with application to Radarsat and SPOT data", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331867

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