Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification
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comunitat-uji-handle2:10234/7036
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http:\\dx.doi.org/10.1109/LGRS.2014.2388133 |
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Title
Fast and Reliable Noise Estimation for Hyperspectral Subspace IdentificationDate
2015-02Publisher
Institute of Electrical and Electronics Engineers (IEEE)Bibliographic citation
BENNER, Peter, et al. Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification. Geoscience and Remote Sensing Letters, IEEE, 2015, vol. 12, no 6, p. 1199-1203.Type
info:eu-repo/semantics/articlePublisher version
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7038190&tag=1Subject
Abstract
In this letter, we introduce an efficient algorithm to estimate the noise correlation matrix in the initial stage of the hyperspectral signal identification by minimum error (HySime) method, commonly used for signal ... [+]
In this letter, we introduce an efficient algorithm to estimate the noise correlation matrix in the initial stage of the hyperspectral signal identification by minimum error (HySime) method, commonly used for signal subspace identification in remotely sensed hyperspectral images. Compared with the current implementations of this stage, the new algorithm for noise estimation relies on the reliable QR factorization, producing correct results even when operating with single-precision arithmetic. Additionally, our algorithm exhibits a lower computational cost, and it is highly parallel. The experiments on a multicore server, using two real hyperspectral scenes, expose that these theoretical advantages carry over to the practical results. [-]
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Geoscience and Remote Sensing Letters, IEEE, 2015, vol. 12, no 6Rights
info:eu-repo/semantics/restrictedAccess
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