Results 51 to 60 of about 1,157 (179)
Performance guarantees for sparse regression-based unmixing
2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015 Sparse regression-based unmixing has received much attention in recent years; however, its theoretical performance has not been explored in the literature. In this work, we present theoretical guarantees for the performance of a sparse regression based unmixing (in short, sparse unmixing) implemented in the form of a Lasso optimization with non ...
Itoh, Yuki +2 more
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A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing [PDF]
Remote Sensing, 2019 Due to the complex background and low spatial resolution of the hyperspectral sensor, observed ground reflectance is often mixed at the pixel level. Hyperspectral unmixing (HU) is a hot-issue in the remote sensing area because it can decompose the observed mixed pixel reflectance.
Zou, Jinlin, Lan, Jinhui
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingIn recent years, hyperspectral sparse unmixing (HSU) has garnered extensive research and attention due to its unique characteristic of not requiring the estimation of endmembers and their number.
Kewen Qu +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing (HSU) is an important technique of remote sensing, which estimates the fractional abundances and the mixing matrix of endmembers in each mixed pixel from the hyperspectral image.
Yuan Yuan, Zihan Zhang, Qi Wang
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Journal of Geophysical Research: Solid Earth, Volume 131, Issue 8, August 2026.Abstract Satellites such as the European Space Agency's Sentinel‐1 constellation allow for the creation of unprecedented volumes of Interferometric Synthetic Aperture RaDAR data that contains both deformation and atmospheric signals. Correction methods have been developed to reduce these atmospheric signals, but they do not generally perform well on ...
M. Gaddes, A. Hooper, S. Ebmeier
wiley +1 more source
Abundance Estimation Methods in Spectral Unmixing for Real Data [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesSpectral unmixing estimates the fractional abundances of materials, having associated spectra called endmembers, in pixels acquired by imaging spectrometers.
D. Cerra, M. Pato, E. Carmona
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Unmixing of Hyperspectral Data Using Spectral Libraries
International Journal of Environment and Geoinformatics, 2020 In hyperspectral images, pixels are found as a mixture of the spectral signatures of several materials, especially when there is an insufficient spatial resolution.
Sefa Küçük, Seniha Esen Yüksel
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Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF
IEEE Access, 2021 Due to the low spatial resolution of the sensors, the hyperspectral images contain mixed pixels. The purpose of hyperspectral unmixing is to decompose the mixed pixels into a series of endmembers and abundance fractions.
Jing Liu, You Zhang, Yi Liu, Caihong Mu
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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 +7 more
wiley +1 more source
Smooth and Sparse Regularization for NMF Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017 In this paper, we propose a matrix factorization method for hyperspectral unmixing using the linear mixing model. In this method, we add the $\arctan$ functions of the endmembers to the $\ell _2$ -norm of the error in order to exploit the sparse property of the fractional abundances.
Yaser Esmaeili Salehani, Saeed Gazor
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