The thesis presents new techniques for classification and unmixing of hyperspectral remote sensing data. The main issues connected to this kind of data (in particular the huge dimension and the possibility to find mixed pixels) have been considered. New advanced techniques have been proposed in order to solve these problems. In a first part, new classification methods based on the use of traditional dimensionality reduction methods (such as Independent Component Analysis - ICA) and on the integration of spatial and spectral information have been proposed. In a second part, methods based on spectral unmixing have been considered to improve the results obtained with classical methods. These methods gave the possibility to improve the spatial resolution of the classification maps thanks to the sub-pixel information which they consider.The main steps of the work are the following:- Introduction and survey of the data. Base assessment: in order to improve the classification of hyperspectral images, data related problems must be considered (very high dimension, presence of mixed pixels)- Development of advanced classification methods making use of classic dimensionality reduction techniques (Independent Component Discriminant Analysis)- Proposition of classification methods exploiting different kinds of contextual information, typical of hyperspectral imagery - Study of spectral unmixing techniques, in order to propose new feature extraction methods exploiting sub-pixel information - Joint use of traditional classification methods and unmixing techniques in order to obtain land cover classification maps at a finer resolutionThe different methods proposed have been tested on several real hyperspectral data, showing results which are comparable or better than methods recently proposed in the literature.
Source separation in hyperspectral imagery