Results 151 to 160 of about 1,111,541 (211)

Automated analysis of ultrastructure through large-scale hyperspectral electron microscopy. [PDF]

Npj Imaging
Duinkerken BHP, Alsahaf AMJ, Hoogenboom JP, Giepmans BNG.   +3 more
europepmc   +1 more source

Assessing Species Fractional Cover and α-Diversity in Boreal Peatlands Across Trophic Levels Using Hyperspectral Data. [PDF]

Ecol Evol
Salko SS, Hovi A, Burdun I, Juola J, Karlqvist S, Rautiainen M.   +5 more
europepmc   +1 more source

Prokaryotic bias in surface ocean particles. [PDF]

Proc Natl Acad Sci U S A
Ryu Y, Maloney AE, Luu VH, Guo L, Oleynik S, Fawcett SE, Higgins MB, Van Oostende N, Ward BB, Cook CCZ, Cohen NR, Ewton E, Menden-Deuer S, Granger J, Marchetti A, Aardema HM, Slagter HA, Schiebel R, Martínez-García A, Haug GH, Sigman DM.   +20 more
europepmc   +1 more source

Noble Gas Isotopes and Nitrogen Isotopologues Reveal Deep Sources and Subsurface Fractionation in Yellowstone Gases. [PDF]

ACS Earth Space Chem
Broadley MW, Barry PH, Tyne RL, Bekaert DV, Karolyte R, Hudak MR, McPaul K, Ramirez CJ, Curtice J, Lloyd KG, Ballentine CJ, Marty B, Young ED, Seltzer AM.   +13 more
europepmc   +1 more source

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Endmember extraction analysis considering endmember variability

2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
In recent years, several kinds of endmember extraction algorithms have been proposed from hyperspectral data set which extracts/selects one single standard endmember spectrum for each existing endmember class or scene component. In this article, endmember variability is considered to the mixture spectrum analysis by representing each endmember by a set
Mingming Xu 0001, Liangpei Zhang 0001, Bo Du 0001   +2 more
openaire   +1 more source

Spatial Preprocessing for Endmember Extraction

IEEE Transactions on Geoscience and Remote Sensing, 2009
Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a remotely sensed hyperspectral scene. These pure signatures are then used to decompose the scene into abundance fractions by means of a spectral unmixing algorithm. Most techniques available in the endmember extraction literature rely on
Maciel Zortea, Antonio Plaza
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Spectral-textural endmember extraction

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010
Several available techniques for endmember extraction and spectral unmixing use only the spectral information contained in the hyperspectral data. In this paper, we introduce a novel method for spatial-spectral endmember extraction which incorporates texture features in the quantification of spatial information (jointly with spectral information ...
Maciel Zortea, Devis Tuia, Fabio Pacifici, Antonio J. Plaza   +3 more
openaire   +1 more source

Improving the quality of extracted endmembers

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009
Endmember extraction for spectral mixture analysis is a necessary step when endmember information is unknown. If endmembers are assumed to be pure pixels present in an image scene, endmember extraction is to search the most distinctive pixels. Popular algorithms using the criteria of simplex volume maximization (e.g., N-FINDR) and spectral signature ...
Qian Du 0001, Liangpei Zhang 0001, Nareenart Raksuntorn   +2 more
openaire   +1 more source

Robust endmember extraction in the presence of anomalies

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Most available methods for endmember extraction use the convexity of the data structure and consider the vertices of the data as the purest pixels. Such methods do not consider the applicability of the linear mixing model once the endmembers have been extracted. Thus they might return false endmem-bers if the data contain outliers such as anomalies. In
Olga Duran, Maria Petrou
openaire   +1 more source