Results 151 to 160 of about 9,243 (189)

Stimulated Raman Scattering Imaging Enabled Biomarker Discovery for Precision Medicine. [PDF]

Chem Biomed Imaging
Li Y, Liu J, Yue S.
europepmc   +1 more source

Transforming hyperspectral data into insight: the DREAM approach for pathology. [PDF]

Biomed Opt Express
Hong M, Koo DES, Junge J, Fraser SE, Cutrale F.   +4 more
europepmc   +1 more source

From spectrum to yield: advances in crop photosynthesis with hyperspectral imaging. [PDF]

Photosynthetica
Panda D, Mohanty S, Das S, Senapaty J, Sahoo DB, Mishra B, Baig MJ, Behera L.   +7 more
europepmc   +1 more source

Estimation of Citrus Leaf Relative Water Content Using CWT Combined with Chlorophyll-Sensitive Bands. [PDF]

Sensors (Basel)
Qi X, Li Y, Dou S, Li W, Yang Y, Wei M.
europepmc   +1 more source

Regularization approaches to hyperspectral unmixing

, 2020
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PolSpec: polarisation-based detection for spectral classification of optical signals

Liu H, Kumar S, Garcia E, French P.
europepmc   +1 more source

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Adversarial Autoencoder Network for Hyperspectral Unmixing

IEEE Transactions on Neural Networks and Learning Systems, 2023
Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e., abundances), has become a major preprocessing technique for the hyperspectral image analysis.
Qiwen Jin, Yong Ma, Fan Fan, Jun Huang, Xiaoguang Mei, Jiayi Ma   +5 more
openaire   +2 more sources

Blind hyperspectral unmixing

SPIE Proceedings, 2007
ABSTRACT Hyperspectral unmixing methods aim at the decompositio n of a hyperspectral image in to a collection endmembersignatures, i.e., the radiance or re”ectance of the materials present in the scene, and the correspondent abundancefractions at each pixel in the image.This paper introduces a new unmixing method termed dependent component analysis ...
Nascimento, Jose, Bioucas-Dias, José M.
openaire   +3 more sources