Hyperspectral Image Segmentation with Markov Chain Model

The Hidden Markov Chain (HMC) model has been extended to take into consideration the multi-component representation of an hyperspectral data cube. Parameters estimation is performed using the general Iterative Conditional Estimation (ICE) method. The vectorial extension of the model is straightforward since the vectorial point of view joints the observation of each pixel as a spectral signature. Then, the segmentation procedure achieves an estimation of multi-dimensional correlated probability density functions (pdf). Multi-dimensional densities have been estimated by a set of 1D densities through a projection step that makes component independent and of reduced dimension. Classifications have been applied to an image from the CASI sensor including 17 bands (from 450 to 950 nm) representing an intensive agricultural region (Brittany, France). Since, the intrinsic dimensionality of the observation has been estimated to 4, the multi-component HMC model has been applied to the CASI image reduced to 4 bands through an adapted projection pursuit method.