Parameters selection of morphological scale-space decomposition for hyperspectral images using tensor modeling

Santiago Velasco-Forero; Jesús Angulo

doi:10.1117/12.850171

12 May 2010 Parameters selection of morphological scale-space decomposition for hyperspectral images using tensor modeling

Santiago Velasco-Forero, Jesús Angulo

Author Affiliations +

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76951B (2010) https://doi.org/10.1117/12.850171
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Santiago Velasco-Forero and Jesús Angulo "Parameters selection of morphological scale-space decomposition for hyperspectral images using tensor modeling", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951B (12 May 2010); https://doi.org/10.1117/12.850171

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