Results 21 to 30 of about 1,165,369 (212)
A multiple endmember mixing model to handle spectral variability
, 2018 This paper proposes a novel mixing model that incorporates spectral variability. The proposed approach relies on the following two ingredients: i) a mixed spectrum is modeled as a combination of a few endmember signatures which belong to some endmember bundles (referred to as classes), ii) sparsity is promoted for the selection of both endmember ...
Uezato, Tatsumi +2 more
core +4 more sources
CO<sub>2</sub> and N<sub>2</sub>O Emissions From Vehicles in Seoul Megacity, South Korea: Insights From Mixing Ratio and Stable Isotope Ratios. [PDF]
Rapid Commun Mass SpectromABSTRACT Rationale Vehicular traffic is a major source of anthropogenic greenhouse gases in megacities; however, real‐world constraints on vehicle‐derived N2O emissions and their isotopic characteristics remain limited. Methods Air samples were collected from three tunnels and urban background sites (a university campus and mountain) in Seoul, South ...
Kim J +7 more
europepmc +2 more sources
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Although Bayesian methods have been very effective for spatial–spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced subpixel mapping (SPM) by simultaneously considering several key issues, i.e., endmember
Yujia Chen +6 more
doaj +1 more source
Geo-spatial Information Science, 2022 Impervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images.
Zhenfeng Shao +4 more
doaj +1 more source
Variability of the endmembers in spectral unmixing: Recent advances [PDF]
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016 Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account.
Drumetz, Lucas +2 more
openaire +3 more sources
A Novel Hyperspectral Unmixing Method based on Least Squares Twin Support Vector Machines
European Journal of Remote Sensing, 2021 In hyperspectral images, endmembers characterizing one class of ground object may vary due to illumination, weathering, slight variations of the materials.
Liguo Wang +3 more
doaj +1 more source
Remote Sensing, 2020 Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set.
Tatsumi Uezato +2 more
doaj +1 more source
Generalized Linear Mixing Model Accounting for Endmember Variability [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability effects resulting mainly from ...
Tales Imbiriba +2 more
openaire +4 more sources
Geophysical Monograph Series, Page 153-168., 2021 Exploring the links between Large Igneous Provinces and dramatic environmental impact
An emerging consensus suggests that Large Igneous Provinces (LIPs) and Silicic LIPs (SLIPs) are a significant driver of dramatic global environmental and biological changes, including mass extinctions.
Yuem Park +3 more
wiley +1 more source
VALIDATION OF EXTRACTED ENDMEMBERS FROM HYPERSPECTRAL IMAGES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 An essential step in the characterization of surface materials using hyperspectral image analysis is image classification using endmembers. Spectral unmixing is the best method for hyperspectral image classification.
A. Sharifi, M. Hosseingholizadeh
doaj +1 more source