In this paper, we cover a decade of research in the field of spectral-spatial classification in hyperspectral remote
sensing. While the very rich spectral information is usually used through pixel-wise classification in order to
recognize the physical properties of the sensed material, the spatial information, with a constantly increasing
resolution, provides insightful features to analyze the geometrical structures present in the picture. This is
especially important for the analysis of urban areas, while this helps reducing the classification noise in other
cases. The very high dimension of hyperspectral data is a very challenging issue when it comes to classification.
Support Vector Machines are nowadays widely aknowledged as a first choice solution. In parallel, catching the
spatial information is also very challenging. Mathematical morphology provides adequate tools: granulometries
(the morphological profile) for feature extraction, advanced filters for the definition of adaptive neighborhoods,
the following natural step being an actual segmentation of the data. In order to merge spectral and spatial
information, different strategies can be designed: data fusion at the feature level or decision fusion combining
the results of a segmentation on the one hand and the result of a pixel wise classification on the other hand.