This work introduces a framework for a fast and algorithmically scalable multiscale representation and segmentation of hyperspectral imagery. The framework is based on the scale-space representation generated by geometric partial differential equations (PDEs) and state of the art numerical methods such as semi-implicit discretization methods, preconditioned conjugated gradient, and multigrid solvers. Multi-scale segmentation of hyperspectral imagery exploits the fact that different image structures exists only at different image scales or resolutions, enabling a better exploitation of the high spatial-spectral information content in hyperspectral imagery. Higher level processes in hyperspectral imagery such as classification, registration, target detection, restoration, and change detection can improve significatively; by working on the regions (objects) identified by the segmentation process, rather than with the image pixels, as it is traditionally done.; The main contribution of this work is the introduction of a framework, where vector-valued geometric scale-spaces are seamlessly integrated with an algorithm for multiscale segmentation of hyperspectral imagery, in a fast and scalable way that makes feasible an object-oriented approach for higher level processes in hyperspectral image processing.
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