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Pregled bibliografske jedinice broj: 748642

Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification


Benner, Peter; Novaković, Vedran; Plaza, Antonio; Quintana- Ortí, Enrique S.; Remón, Alfredo
Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification // IEEE geoscience and remote sensing letters, 12 (2015), 6; 1199-1203 doi:10.1109/LGRS.2014.2388133 (međunarodna recenzija, članak, znanstveni)


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Naslov
Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification

Autori
Benner, Peter ; Novaković, Vedran ; Plaza, Antonio ; Quintana- Ortí, Enrique S. ; Remón, Alfredo

Izvornik
IEEE geoscience and remote sensing letters (1545-598X) 12 (2015), 6; 1199-1203

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
hyperspectral imaging; subspace identification; noise estimation; least square problems; multicore processors

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Geologija, Računarstvo



POVEZANOST RADA


Projekti:
037-1193086-2771 - Numeričke metode u geofizičkim modelima (Singer, Saša, MZOS ) ( CroRIS)

Ustanove:
Prirodoslovno-matematički fakultet, Matematički odjel, Zagreb

Profili:

Avatar Url Vedran Novaković (autor)

Poveznice na cjeloviti tekst rada:

doi dx.doi.org ieeexplore.ieee.org

Citiraj ovu publikaciju:

Benner, Peter; Novaković, Vedran; Plaza, Antonio; Quintana- Ortí, Enrique S.; Remón, Alfredo
Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification // IEEE geoscience and remote sensing letters, 12 (2015), 6; 1199-1203 doi:10.1109/LGRS.2014.2388133 (međunarodna recenzija, članak, znanstveni)
Benner, P., Novaković, V., Plaza, A., Quintana- Ortí, E. & Remón, A. (2015) Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification. IEEE geoscience and remote sensing letters, 12 (6), 1199-1203 doi:10.1109/LGRS.2014.2388133.
@article{article, author = {Benner, Peter and Novakovi\'{c}, Vedran and Plaza, Antonio and Quintana- Ort\'{\i}, Enrique S. and Rem\'{o}n, Alfredo}, year = {2015}, pages = {1199-1203}, DOI = {10.1109/LGRS.2014.2388133}, keywords = {hyperspectral imaging, subspace identification, noise estimation, least square problems, multicore processors}, journal = {IEEE geoscience and remote sensing letters}, doi = {10.1109/LGRS.2014.2388133}, volume = {12}, number = {6}, issn = {1545-598X}, title = {Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification}, keyword = {hyperspectral imaging, subspace identification, noise estimation, least square problems, multicore processors} }
@article{article, author = {Benner, Peter and Novakovi\'{c}, Vedran and Plaza, Antonio and Quintana- Ort\'{\i}, Enrique S. and Rem\'{o}n, Alfredo}, year = {2015}, pages = {1199-1203}, DOI = {10.1109/LGRS.2014.2388133}, keywords = {hyperspectral imaging, subspace identification, noise estimation, least square problems, multicore processors}, journal = {IEEE geoscience and remote sensing letters}, doi = {10.1109/LGRS.2014.2388133}, volume = {12}, number = {6}, issn = {1545-598X}, title = {Fast and Reliable Noise Estimation for Hyperspectral Subspace Identification}, keyword = {hyperspectral imaging, subspace identification, noise estimation, least square problems, multicore processors} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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  • INSPEC


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