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Unmixing hyperspect...
Unmixing hyperspectral data by using signal subspace sampling
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- Spiegelberg, Jakob (författare)
- Uppsala universitet,Materialteori
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- Muto, Shunsuke (författare)
- Nagoya Univ, Inst Mat & Syst Sustainabil, Adv Measurement Technol Ctr, Chikusa Ku, Nagoya, Aichi 4648603, Japan.
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- Ohtsuka, Masahiro (författare)
- Nagoya Univ, Grad Sch Engn, Chikusa Ku, Nagoya, Aichi 4648603, Japan.
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- Pelckmans, Kristiaan (författare)
- Uppsala universitet,Avdelningen för systemteknik,Reglerteknik
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- Rusz, Jan, 1979- (författare)
- Uppsala universitet,Materialteori
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(creator_code:org_t)
- ELSEVIER SCIENCE BV, 2017
- 2017
- Engelska.
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Ingår i: Ultramicroscopy. - : ELSEVIER SCIENCE BV. - 0304-3991 .- 1879-2723. ; 182, s. 205-211
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- This paper demonstrates how Signal Subspace Sampling (SSS) is an effective pre-processing step for Non-negative Matrix Factorization (NMF) or Vertex Component Analysis (VCA). The approach allows to uniquely extract non-negative source signals which are orthogonal in at least one observation channel, respectively. It is thus well suited for processing hyperspectral images from X-ray microscopy, or other emission spectroscopies, into its non-negative source components. The key idea is to resample the given data so as to satisfy better the necessity and sufficiency conditions for the subsequent NMF or VCA. Results obtained both on an artificial simulation study as well as based on experimental data from electronmicroscopy are reported.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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