Results 1 to 10 of about 6,067 (165)
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 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
doaj +1 more source
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
doaj +1 more source
Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing
Remote Sensing, 2021 Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult ...
Ziqiang Hua +3 more
doaj +1 more source
DLR HySU—A Benchmark Dataset for Spectral Unmixing
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
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 With the support of spectral libraries, sparse unmixing techniques have gradually developed. However, some existing sparse unmixing algorithms suffer from problems, such as insufficient utilization of spatial information and sensitivity to noise.
Yao Liang +4 more
doaj +1 more source
Applied Sciences, 2022 The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI).
Baohua Jin +4 more
doaj +1 more source
Spectral Unmixing With Multiple Dictionaries [PDF]
IEEE Geoscience and Remote Sensing Letters, 2018 Spectral unmixing aims at recovering the spectral signatures of materials, called endmembers, mixed in a hyperspectral or multispectral image, along with their abundances. A typical assumption is that the image contains one pure pixel per endmember, in which case spectral unmixing reduces to identifying these pixels.
Jeremy E. Cohen, Nicolas Gillis
openaire +3 more sources