Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44977
Title: On the use of ritz values for calculating the number of endmembers in hyperspectral images
Authors: Guerra, Raúl 
López, Sebastián 
Callico, Gustavo M. 
Lopez, Jose F. 
Sarmiento, Roberto 
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
Keywords: Hyperspectral imaging
Eigenvalues and eigenfunctions
Signal to noise ratio
Estimation
Issue Date: 2017
Journal: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Conference: 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 
Abstract: Signal subspace identification is a crucial step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. This is due to the fact that a correct dimensionality reduction improves algorithm performances and reduces their complexity and data requirements. This paper introduces a new method for this task which is based on the Ritz values obtained in methods such as the restarted Arnoldi method or the Lanczos method for calculating the eigenvalues and eigenvectors of a given matrix. In particular, it first calculates a high number of eigenvalues and eigenvectors, and a Ritz value per eigenvalue calculated and then, it estimates the dimension of the signal subspace according to the Ritz values obtained. The results obtained with the proposed method for synthetic and real hyperspectral images verify the performance of the introduced methodology to estimate the number of endmembers in different types of hyperspectral images. Moreover, these results are better than the ones obtained, for the same images, with the most popular algorithm for this task in the state of the art, i.e. the HySIME and the Virtual Dimensionality algorithms.
URI: http://hdl.handle.net/10553/44977
ISBN: 9781467390125
ISSN: 2158-6276
DOI: 10.1109/WHISPERS.2014.8077553
Source: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing[ISSN 2158-6276],v. 2014-June (8077553)
Appears in Collections:Actas de congresos
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