The processing of hyperspectral images, seen as functions that link each pixel to a curve, has become crucial, in remote sensing applications for instance. Here we tackle the problem of segmentation of such images, by carefully combining image processing tools and functional statistics, namely a Potts model and a likelihood term based on functional kernel density estimation in a Bayesian framework, and consider possible extensions.