  The present paper develops a general methodology for the morphological
segmentation of hyperspectral images, i.e., with an important number of
channels. This approach, based on watershed, is composed of a spectral
classification to obtain the markers and a vectorial gradient which gives the
spatial information. Several alternative gradients are adapted to the different
hyperspectral functions. Data reduction is performed either by Factor Analysis
or by model fitting. Image segmentation is done on different spaces: factor
space, parameters space, etc. On all these spaces the spatial/spectral
segmentation approach is applied, leading to relevant results on the image.
