7 May 2007 Fast multiscale regularization and segmentation of hyperspectral imagery via anisotropic diffusion and algebraic multigrid solvers
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Abstract
This paper presents an algorithm that generates a scale-space representation of hyperspectral imagery using Algebraic Multigrid (AMG) solvers. The scale-space representation is obtained by solving with AMG a vector-valued anisotropic diffusion equation, with the hyperspectral image as its initial condition. AMG also provides the necessary structure to obtain a hierarchical segmentation of the image. The scale space representation of the hyperspectral image can be segmented in linear time complexity. Results in the paper show that improved segmentation is achieved. The proposed methodology to solve vector PDEs can be used to extend a number of techniques currently being developed for the fast computation of geometric PDEs and its application for the processing of hyperspectral and multispectral imagery.
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Julio M. Duarte-Carvajalino, Julio M. Duarte-Carvajalino, Guillermo Sapiro, Guillermo Sapiro, Miguel Vélez--Reyes, Miguel Vélez--Reyes, Paul Castillo, Paul Castillo, "Fast multiscale regularization and segmentation of hyperspectral imagery via anisotropic diffusion and algebraic multigrid solvers", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656512 (7 May 2007); doi: 10.1117/12.721036; https://doi.org/10.1117/12.721036
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