Results 161 to 170 of about 68,035 (215)

High-Content SRS Imaging Unveils Altered Cholesterol Metabolism in Ovarian Cancers Under CAR-T Treatment. [PDF]

Adv Sci (Weinh)
Prabhu Dessai CV, Huang Z, Lin H, Ding G, He H, Siddiqui M, Zhang M, Wong W, Cheng JX.   +8 more
europepmc   +1 more source

SIP-SRS Imaging of Cell Wall Synthesis Identifies a Synergy between Micafungin and Amphotericin B. [PDF]

Adv Sci (Weinh)
Zhang M, Zhan Y, Lin H, Chen J, Seleem MN, Mansour M, Cheng JX.   +6 more
europepmc   +1 more source

Detection of asbestos-based cement rooftops in conflict-affected settings using EnMAP hyperspectral data: a research article. [PDF]

Sci Rep
Shepherd JE, Sagi E, Zagron G, Ben-Dor E.   +3 more
europepmc   +1 more source

Compressed sensing based hyperspectral unmixing

2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014
In hyperspectral images the measured spectra for each pixel can be modeled as convex combination of small number of endmember spectra. Since the measured structure contains only a few of possible responses out of possibly many materials sparsity based convex optimization techniques or compressive sensing can be used for hyperspectral unmixing.
R. Tufan Albayrak, Ali Cafer Gürbüz, Bertan Günyel   +2 more
openaire   +4 more sources

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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

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
openaire   +3 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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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
openaire   +1 more source