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Conference papers
Parameters Selection Of Morphological Scale-Space Decomposition For Hyperspectral Images Using Tensor Modeling
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.
Cited literature [15 references]
https://hal-mines-paristech.archives-ouvertes.fr/hal-00834484
Contributor : Bibliothèque Mines Paristech <>
Submitted on : Saturday, June 15, 2013 - 3:28:31 PM
Last modification on : Thursday, September 24, 2020 - 4:38:04 PM
Long-term archiving on: : Monday, September 16, 2013 - 4:04:44 AM
Contributor : Bibliothèque Mines Paristech <>
Submitted on : Saturday, June 15, 2013 - 3:28:31 PM
Last modification on : Thursday, September 24, 2020 - 4:38:04 PM
Long-term archiving on: : Monday, September 16, 2013 - 4:04:44 AM
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VelascoAngulo_SPIE10.pdf
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