Results 21 to 30 of about 6,533 (191)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The autoencoder (AE) framework is usually adopted as a baseline network for hyperspectral unmixing. Totally an AE performs well in hyperspectral unmixing through automatically learning low-dimensional embedding and reconstructing data.
Xiao Chen +5 more
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Spectral Unmixing of Pigments on Surface of Painted Artefacts Considering Spectral Variability [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesPainted artefacts, such as murals and paintings, are the treasures of human civilization. Pigment is an important component of their surfaces. It is crucial to study the composition and proportion of pigments on the surface of painted artefacts for the ...
Y. Wang +10 more
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Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image.
Fan Li +5 more
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A Probabilistic Approach to Spectral Unmixing [PDF]
, 2010 In this paper, we present a statistical approach to spectral unmixing with unknown endmember spectra and unknown illuminant power spectrum. The method presented here is quite general in nature, being applicable to settings in which sub-pixel information is required.
Huynh, Cong, Robles-Kelly, Antonio
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Robust spectral unmixing for anomaly detection [PDF]
2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014 This paper is concerned with a joint Bayesian formulation for determining the endmembers and abundances of hyperspectral images along with sparse outliers which can lead to estimation errors unless accounted for. We present an inference method that generalizes previous work and provides a MCMC estimate of the posterior distribution. The proposed method
Gregory E. Newstadt +2 more
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Nonlinear unmixing of minerals based on the log and continuum removal model
European Journal of Remote Sensing, 2019 Spectral mixing models for minerals can be complex, and choosing the right unmixing model is indispensable to ensure the accuracy of spectral unmixing. Continuum removal (CR) and natural log operation have the potential to eliminate nonlinear effects in ...
Hengqian Zhao, Xuesheng Zhao
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Mixed Fluids of Water and Carbon Dioxide
Geophysical Monograph Series, Page 237-243., 2020 This book is Open Access. A digital copy can be downloaded for free from Wiley Online Library.
Explores the behavior of carbon in minerals, melts, and fluids under extreme conditions
Carbon trapped in diamonds and carbonate-bearing rocks in subduction zones are examples of the continuing exchange of substantial carbon ...
Evan Abramson
wiley +1 more source
Sensors, 2020 Multispectral photoacoustic imaging has been widely explored as an emerging tool to visualize and quantify tissue chromophores noninvasively. This modality can capture the spectral absorption signature of prominent tissue chromophores, such as oxygenated,
Valeria Grasso +2 more
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Spectral Unmixing with Sparsity and Structuring Constraints [PDF]
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018 This paper addresses the linear spectral unmixing problem, by incorporating different constraints that may be of interest in order to cope with spectral variability: sparsity (few nonzero abundances), group exclusivity (at most one nonzero abundance within subgroups of endmembers) and significance (non-zero abundances must exceed a threshold).
Ramzi Ben Mhenni +3 more
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On the use of overcomplete dictionaries for spectral unmixing [PDF]
2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), 2012 Hyperspectral unmixing is a sub pixel classification method which aims at recovering fraction and type of materials mixed in a single pixel. This work addresses the unmixing problem from the compressive sensing point of view by using overcomplete dictionaries enabling automatization of the process.
Bieniarz, Jakub +3 more
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