Geometric scale-space framework for the analysis of hyperspectral imagery.

详细信息   

• 作者：Duarte-Carvajalino ; Julio Martin.
• 学历：Doctor
• 年：2008
• 导师：Velez-Reyes, Miguel
• 毕业院校：University of Puerto Rico
• 专业：Computer Science.
• ISBN：9780549408529
• CBH：3296049
• Country：Puerto Rico
• 语种：English
• FileSize：3230636
• Pages：200

文摘

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.