Parameters Selection Of Morphological Scale-Space Decomposition For Hyperspectral Images Using Tensor Modeling

Santiago Velasco-Forero 1 Jesus Angulo 1
1 CMM - Centre de Morphologie Mathématique
Abstract : Dimensionality reduction (DR) using tensor structures in morphological scale-space decomposition (MSSD) for HSI has been investigated in order to incorporate spatial information in DR.We present results of a comprehensive investigation of two issues underlying DR in MSSD. Firstly, information contained in MSSD is reduced using HOSVD but its nonconvex formulation implicates that in some cases a large number of local solutions can be found. For all experiments, HOSVD always reach an unique global solution in the parameter region suitable to practical applications. Secondly, scale parameters in MSSD are presented in relation to connected components size and the influence of scale parameters in DR and subsequent classification is studied.
Keywords : Unsupervised feature extraction mathematical morphology tensor analysis dimensional reduction classification principal component hyperspectral imagery
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Communication dans un congrès
Sylvia S. Shen and Paul E. Lewis. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, May 2010, Orlando, United States. SPIE - The International Society for Optical Engineering, 7695B, 12 p., 2010, 〈10.1117/12.850171〉
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Dernière modification le : mardi 27 mars 2018 - 16:06:13
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Santiago Velasco-Forero, Jesus Angulo. Parameters Selection Of Morphological Scale-Space Decomposition For Hyperspectral Images Using Tensor Modeling. Sylvia S. Shen and Paul E. Lewis. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, May 2010, Orlando, United States. SPIE - The International Society for Optical Engineering, 7695B, 12 p., 2010, 〈10.1117/12.850171〉. 〈hal-00834484〉

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