Results 1 to 10 of about 6,533 (191)
SUFI: an automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and post-mortem human brain tissues [PDF]
BMC Neuroscience, 2023 Background Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample.
Vijay Sadashivaiah +9 more
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DLR HySU—A Benchmark Dataset for Spectral Unmixing [PDF]
Remote Sensing, 2021 Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers.
Daniele Cerra +10 more
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Spectral Unmixing of Hyperspectral Remote Sensing Imagery via Preserving the Intrinsic Structure Invariant [PDF]
Sensors, 2018 Hyperspectral unmixing, which decomposes mixed pixels into endmembers and corresponding abundance maps of endmembers, has obtained much attention in recent decades. Most spectral unmixing algorithms based on non-negative matrix factorization (NMF) do not
Yang Shao +3 more
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Remote Sensing, 2018 Spectral unmixing is a popular technique for hyperspectral data interpretation. It focuses on estimating the abundance of pure spectral signature (called as endmembers) in each observed image signature.
Xiangrong Zhang +6 more
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Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning. [PDF]
PLoS ONE, 2019 Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection.
Tristan D McRae +3 more
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Fuzzy Assessment of Spectral Unmixing Algorithms
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014 While a single spectrum is often used to present a pure class, it is more realistic to consider intra-class spectral variation and model a pure class using a group of its representative spectra. In line with this consideration, crisp unmixing accuracy assessment, where unmixing performance is assessed using a mean squared error of the estimated ...
Xiuping Jia, Liguo Wang
exaly +2 more sources
Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning
Remote Sensing, 2019 Spectral unmixing extracts subpixel information by decomposing observed pixel spectra into a collection of constituent spectra signatures and their associated fractions.
Zeng Li, Jie Chen, Susanto Rahardja
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A blind spectral unmixing in wavelet domain [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral data - Systems, Processing, Information ...
S. S. Vijayashekhar, Jignesh S. Bhatt
openaire +3 more sources
Remote Sensing, 2023 Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by ...
Ying Cheng +3 more
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SPATIAL INTERPOLATION AS A TOOL FOR SPECTRAL UNMIXING OF REMOTELY SENSED IMAGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, but it is too complex to implement for common users who are interested in land cover mapping.
L. Xi, C. Xiaoling
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