Results 51 to 60 of about 1,511 (182)

Collaborative Sparse Regression for Hyperspectral Unmixing [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2014
Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer).
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
openaire   +1 more source

Red‐to‐Near‐Infrared Fluorescence Resonance Energy Transfer Reporters for Dual Imaging Applications

Small Methods, Volume 10, Issue 15, 7 August 2026.
Genetically encoded FRET biosensors reveal dynamic signaling in living cells, but overlapping spectra have limited the simultaneous monitoring of multiple pathways. We introduce a red‐to‐near infrared FRET pair, mRuby2/miRFP670nano3, compatible with existing reporters and enabling dual FLIM‐FRET without bleed‐through.
Van Thi‐Hong Tran, Frans Ek, Hasan Yassine, Rajalaxmi Behera, Vesa P. Hytönen, James R. W. Conway   +5 more
wiley   +1 more source

DISTRIBUTED UNMIXING OF HYPERSPECTRAL DATAWITH SPARSITY CONSTRAINT [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017
Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in
S. Khoshsokhan, R. Rajabi, H. Zayyani
doaj   +1 more source

SMILE: Extraction‐free submicron‐resolution mapping of lipid chain length and unsaturation by stimulated Raman imaging

VIEW, Volume 7, Issue 4, August 2026.
In this work, we develop submicron‐resolution mapping of intracellular lipid elements (SMILE) as an extraction‐free vibrational spectroscopic imaging platform based on hyperspectral stimulated Raman scattering microscopy with a spectral analysis pipeline for pixel‐resolved lipid profiling.
Yihui Zhou, Xiangjie Huang, Xingxin Liu, Ling Zhang, Jie Huang, Jing Zhao, Yongqing Zhang, Jin Zhang, Yanqing Gong, Delong Zhang, Hyeon Jeong Lee   +10 more
wiley   +1 more source

Conventional to Deep Learning Methods for Hyperspectral Unmixing: A Review

Remote Sensing
Hyperspectral images often contain many mixed pixels, primarily resulting from their inherent complexity and low spatial resolution. To enhance surface classification and improve sub-pixel target detection accuracy, hyperspectral unmixing technology has ...
Jinlin Zou, Hongwei Qu, Peng Zhang
doaj   +1 more source

Surface Mineralogy and Hydrological Controls on “Hotspots” of Dust Emission at Etosha Pan, Namibia

Journal of Geophysical Research: Earth Surface, Volume 131, Issue 8, August 2026.
Abstract Ephemeral lake beds are globally significant sources of atmospheric mineral dust aerosols, yet emissions from these landforms exhibit considerable spatial and temporal variability. The complex interactions between climatic drivers and surface properties that govern dust emissions remain poorly understood, contributing to uncertainties in model
N. S. Wallum, R. G. Bryant, G. F. S. Wiggs, R. L. Reynolds, H. L. Goldstein, J. A. Leidelmeijer, J. M. Nield, M. C. Baddock   +7 more
wiley   +1 more source

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Remote Sensing, 2019
Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression.
Ruyi Feng, Lizhe Wang, Yanfei Zhong
doaj   +1 more source

Fast Unmixing of Noisy Hyperspectral Images Based on Vertex Component Analysis and Singular Spectrum Analysis Algorithms

Canadian Journal of Remote Sensing, 2020
Efficient denoising is of great significance to unmixing hyperspectral images. In the present study, a fast unmixing method for noisy hyperspectral images based on the combination of vertex component analysis and singular spectrum analysis is proposed ...
Dongmei Song, Ning Sun, Mingming Xu, Bin Wang, Ling Zhang   +4 more
doaj   +1 more source

Simplex‐based model for nanoparticle grain identification in four‐dimensional scanning transmission electron microscopy data

Journal of Microscopy, Volume 303, Issue 2, Page 194-215, August 2026.
Abstract Grain identification in polycrystalline nanoparticles, for example, determining which crystal phases are present at each spatial location, is fundamental to materials characterisation. This is particularly challenging when grains overlap extensively, as commonly occurs in four‐dimensional scanning transmission electron microscopy (4D‐STEM ...
Wei Liu, Roberto dos Reis, Chad A. Mirkin, Vinayak P. Dravid, Wei Chen, Daniel W. Apley   +5 more
wiley   +1 more source

CoralSpec‐30M: A large‐scale coral spectral dataset covering multiple species, scales, and health conditions

Limnology and Oceanography Letters, Volume 11, Issue 4, July 2026.
Abstract Coral reef monitoring techniques are in high demand to support conservation efforts in marine ecosystems. Noninvasively measuring the spectral reflectance of corals provides rich information about their health status. However, the lack of large‐scale, high‐quality coral spectral datasets prevents the application of data‐driven spectral ...
Kaizhang Kang, Wolfgang Heidrich
wiley   +1 more source