Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Englin, Wong"'
Autor:
Aaron, Kiely, Matthew, Klimesh, Ian, Blanes, Jonathan, Ligo, Magli, Enrico, Nazeeh, Aranki, Michael, Burl, Roberto, Camarero, Michael, Cheng, Sam, Dolinar, David, Dolman, Greg, Flesch, Hamid, Ghassemi, Martin, Gilbert, Miguel, Hernández-Cabronero, Didier, Keymeulen, Martin, Le, Huy, Luong, Christopher, Mcguinness, Gilles, Moury, Thang, Pham, Martin, Plintovic, Frederic, Sala, Lucana, Santos, Alan, Schaar, Joan, Serra-Sagristà, Simon, Shin, Brenton, Sundlie, Valsesia, Diego, Raffaele, Vitulli, Englin, Wong, William, Wu, Hua, Xie, Hanying, Zhou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2153::6c1313a7c3fa6dc1f9b2095799a05a13
http://hdl.handle.net/11583/2728880
http://hdl.handle.net/11583/2728880
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 6:531-553
Linear spectral mixture analysis (LSMA) is a theory that can be used to perform spectral unmixing where three major LSMA techniques, least squares orthogonal subspace projection (LSOSP), non-negativity constrained least squares (NCLS) and fully const
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0527af3a59970ab7b9d53aa02cccec95
https://doi.org/10.1109/jstars.2012.2234441
https://doi.org/10.1109/jstars.2012.2234441
Publikováno v:
Hyperspectral Data Processing: Algorithm Design and Analysis
Linear spectral mixture analysis (LSMA) has been widely used in remote sensing community for spectral unmixing. This letter develops a promising technique, called kernel-based LSMA (KLSMA), which uses nonlinear kernels to resolve the issue of nonline
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::93b136b961454f3f7dfb054f1d581b46
https://doi.org/10.1109/lgrs.2011.2162088
https://doi.org/10.1109/lgrs.2011.2162088
Autor:
Chein-I Chang, Englin Wong
Publikováno v:
High-Performance Computing in Remote Sensing II.
Abundance fully constrained least squares (FLCS) method has been widely used for spectral unmixing. A modified FCLS (MFCLS) was previously proposed for the same purpose to derive two iterative equations for solving fully abundance-constrained spectra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4c6cfefa8d2c3b36e720a0824691949
https://doi.org/10.1117/12.979187
https://doi.org/10.1117/12.979187
Publikováno v:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII.
Linear Spectral Mixture Analysis (LSMA) is a theory developed to perform spectral unmixing where three major LSMA techniques, Least Squares Orthogonal Subspace Projection (LSOSP), Non-negativity Constrained Least Squares (NCLS) and Fully Constrained
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2285d3d32b8fad513043919f9b2765fc
https://doi.org/10.1117/12.884650
https://doi.org/10.1117/12.884650
Publikováno v:
WHISPERS
Linear Spectral Mixture Analysis (LSMA) has been widely used in remote sensing community. Recently, kernel-based approaches have received considerable interest in hyperspectral image analysis where nonlinear kernels are used to resolve the issue of n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08ee6802602bafc0e9bc2a47ae7eca7f
https://doi.org/10.1109/whispers.2009.5289096
https://doi.org/10.1109/whispers.2009.5289096
Autor:
Chein-I Chang, Mark Englin Wong
Publikováno v:
SPIE Proceedings.
Since Magnetic Resonance (MR) images can be considered as multispectral images where each spectral band image is acquired by a particular pulse sequence, this paper investigates an application of a technique that is widely used in multispectral image
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b14fcf93647e5c4bf09cb3909fe64b7
https://doi.org/10.1117/12.782220
https://doi.org/10.1117/12.782220
Autor:
Yong-Kie Wong, Jyh-Wen Chai, Ching-Wen Yang, Shih-Yu Chen, Hsian-Min Chen, San-Kan Lee, Clayton Chi-Chang Chen, Englin Wong, Chein-I Chang
Publikováno v:
International Journal of Computational Science and Engineering. 8:87
Magnetic resonance MR image analysis is generally performed by spatial domainbased image processing, referred to as inter-pixel image processing, which takes advantage of spatial correlation among sample pixels. Unfortunately, in many areas, several
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8edf6517e48aab7080007d7a66924f70
https://doi.org/10.1504/ijcse.2013.053090
https://doi.org/10.1504/ijcse.2013.053090
Publikováno v:
2009 First Workshop on Hyperspectral Image & Signal Processing: Evolution in Remote Sensing; 2009, p1-4, 4p