Results 131 to 140 of about 1,511 (182)

PolSpec : Polarisation-Based Detection for Versatile, Cost-Effective Rapid Hyperspectral Imaging. [PDF]

J Biophotonics
Liu H, Kumar S, Garcia E, Ennis D, McNeish IA, French PMW.   +5 more
europepmc   +1 more source

Multiscale and Multimodal Image Fusion. Coping with Differences in Scanned Area and Spatial Resolution for Raman/Fluorescence Images of Labeled Cells. [PDF]

Anal Chem
Sicre-Conesa A, Marsal M, Gómez-Sánchez A, Loza-Álvarez P, de Juan A.   +4 more
europepmc   +1 more source

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Sparse Distributed Multitemporal Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing, 2017
Blind hyperspectral unmixing jointly estimates spectral signatures and abundances in hyperspectral ima-ges (HSIs). Hyperspectral unmixing is a powerful tool for analyzing hyperspectral data. However, the usual huge size of HSIs may raise difficulties for classical unmixing algorithms, namely, due to limitations of the hardware used.
Johannes R Sveinsson, J Bioucas-Dias, Magnus Ulfarsson   +2 more
exaly   +2 more sources

Cauchy NMF for Hyperspectral Unmixing

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Non-negative matrix factorization (NMF) is a classical hyperspectral unmixing model which minimizes the Euclidean distance between the hyperspectral data matrix and its low rank approximation (i.e., the product of endmember matrix and abundance matrix), and it fails when applied to noisy data because the loss function is sensitive to outliers.
Jiangtao Peng, Fan Jiang, Weiwei Sun 0005, Yicong Zhou   +3 more
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Parallel Hyperspectral Unmixing on GPUs

IEEE Geoscience and Remote Sensing Letters, 2014
This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions.
José M. P. Nascimento, José M. Bioucas-Dias, José M. Rodriguez Alves, Vítor Silva 0001, Antonio Plaza   +4 more
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Semi-supervised hyperspectral unmixing

2014 IEEE Geoscience and Remote Sensing Symposium, 2014
In this paper, an effective method is proposed that combines supervised and unsupervised unmixing. We assume a linear model for the hyperspectral data and incorporate information about endmembers that are known to be in the data into the model. This information can be acquired from a spectral library or extracted from the data.
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
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Fast multitemporal hyperspectral unmixing

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
In this paper, we present a fast blind multitemporal hyperspectral unmixing algorithm, using an l 1 penalty to promote sparse abundances. The method is able to account for different acquisition conditions of multitemporal images, by allowing the spectral signatures in the different temporal images to vary. The new algorithm is tested on simulated data
Jakob Sigurdsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
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Unmixing sparse hyperspectral mixtures

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Finding an accurate sparse approximation of a spectral vector described by a linear model, when there is available a library of possible constituent signals (called endmembers or atoms), is a hard combinatorial problem which, as in other areas, has been increasingly addressed. This paper studies the efficiency of the sparse regression techniques in the
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
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Superpixel construction for hyperspectral unmixing

2018 26th European Signal Processing Conference (EUSIPCO), 2018
Spectral unmixing aims to determine the component materials and their associated abundances from mixed pixels in a hyperspectral image. Instead of performing unmixing independently on each pixel, investigating spatial and spectral correlations among pixels can be beneficial to enhance the unmixing performance.
Zeng Li 0001, Jie Chen 0022, Susanto Rahardja   +2 more
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An Antinoise Method for Hyperspectral Unmixing

IEEE Geoscience and Remote Sensing Letters, 2015
In this letter, we propose an antinoise method for hyperspectral unmixing. In the antinoise method, all noises are addressed. The following techniques are applied: 1) an endmember dictionary is constructed first to initialize the solution; 2) an approximated L 0 norm constraint is employed to prune the dictionary and fulfill the sparse coding; and 3)
Chunzhi Li, Aimin Zhou, Guixu Zhang, Faming Fang   +3 more
openaire   +1 more source