Duarte-Carvajalino, Julio M.

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    Geometric scale-space framework for the analysis of hyperspectral imagery
    (2007) Duarte-Carvajalino, Julio M.; Vélez-Reyes, Miguel; College of Engineering; Hunt, Shawn D.; Rivera Gallego, Wilson; Castillo, Paul E.; Sapiro, Guillermo; Department of Electrical and Computer Engineering; Gilbes, Fernando
    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.