This talk gives an overview about discriminative and reconstructive classification methods for remote sensing images. In the first part, the most commonly used remote sensing sensors and their value for geoscientific applications are introduced. The second part of the presentation explains the discriminative and reconstructive model component of classifiers. While reconstructive methods are able to provide valuable posterior probabilities and are especially suitable for incremental/sequential learning, discriminative models mostly achieve a higher classification accuracy. This talk will present some advantages that arise when both components are combined and used for classification. Applications mainly focus on landcover classification of multispectral and hyperspectral satellite images.

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