Results 31 to 40 of about 1,362 (207)
Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery [PDF]
Remote SensingWe introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM).
John Waczak, David J. Lary
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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
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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
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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
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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
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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
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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
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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
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Remote Sensing, 2022 Hyperspectral remote sensing image (HSI) include rich spectral information that can be very beneficial for change detection (CD) technology. Due to the existence of many mixed pixels, pixel-wise approaches can lead to considerable errors in the resulting
Haishan Li, Ke Wu, Ying Xu
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Subpixel Mapping of Surface Water in the Tibetan Plateau with MODIS Data
Remote Sensing, 2020 This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS).
Chenzhou Liu +4 more
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