Hyper-Spectral Imaging (HIS) also known as chemical or spectroscopic imaging is an emerging technique that combines
imaging and spectroscopy to capture both spectral and spatial information from an object. Hyperspectral images are
made up of contiguous wavebands in a given spectral band. These images provide information on the chemical
make-up profile of objects, thus allowing the differentiation of objects of the same colour but which possess make-up
profile. Yet, whatever the application field, most of the methods devoted to HIS processing conduct data analysis without
taking into account spatial information.Pixels are processed individually, as an array of spectral data without any spatial
structure. Standard classification approaches are thus widely used (k-means, fuzzy-c-means hierarchical
classification...). Linear modelling methods such as Partial Least Square analysis (PLS) or non linear approaches like
support vector machine (SVM) are also used at different scales (remote sensing or laboratory applications). However,
with the development of high resolution sensors, coupled exploitation of spectral and spatial information to process
complex images, would appear to be a very relevant approach. However, few methods are proposed in the litterature.
The most recent approaches can be broadly classified in two main categories. The first ones are related to a direct
extension of individual pixel classification methods using just the spectral dimension (k-means, fuzzy-c-means or FCM,
Support Vector Machine or SVM). Spatial dimension is integrated as an additionnal classification parameter (Markov
fields with local homogeneity constrainst [5], Support Vector Machine or SVM with spectral and spatial kernels
combination [2], geometrically guided fuzzy C-means [3]...). The second ones combine the two fields related to each
dimension (spectral and spatial), namely chemometric and image analysis. Various strategies have been attempted. The
first one is to rely on chemometrics methods (Principal Component Analysis or PCA, Independant Component Analysis or
ICA, Curvilinear Component Analysis...) to reduce the spectral dimension and then to apply standard images processing technics on the resulting score images i.e. data projection on a subspace. Another approach is to extend the definition
of basic image processing operators to this new dimensionality (morphological operators for example [1, 4]).
However, the approaches mentioned above tend to favour only one description either directly or indirectly (spectral or
spatial). The purpose of this paper is to propose a hyperspectral processing approach that strikes a better balance in the
treatment of both kinds of information....Cet article présente une stratégie de segmentation d’images hyperspectrales liant de façon symétrique et
conjointe les aspects spectraux et spatiaux. Pour cela, nous proposons de construire des variables latentes
permettant de définir un sous-espace représentant au mieux la topologie de l’image. Dans cet article, nous
limiterons cette notion de topologie à la seule appartenance aux régions. Pour ce faire, nous utilisons d’une
part les notions de l’analyse discriminante (variance intra, inter) et les propriétés des algorithmes de
segmentation en région liées à celles-ci. Le principe générique théorique est exposé puis décliné sous la
forme d’un exemple d’implémentation optimisé utilisant un algorithme de segmentation en région type split
and merge. Les résultats obtenus sur une image de synthèse puis réelle sont exposés et commentés