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Variational Autoencoders for Hyperspectral Unmixing with Endmember Variability
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021Spectral signatures are usually affected by variations in environmental conditions. The spectral variability is thus one of the most important and challenging problems to be addressed in hyperspectral unmixing. Generally, it is a non-trivial task to model the endmember variability, and existing spectral unmixing methods that address the spectral ...
Shuaikai Shi +3 more
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IEEE Transactions on Geoscience and Remote Sensing, 2016
Spectral variability, unrelated to the purity of endmembers, can change the geometry of the dataspace and affect conventional methods used to identify endmembers. Several methods have been developed to identify and extract endmember bundles representing the spectral variability within each endmember class.
Arman Melkumyan +2 more
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Spectral variability, unrelated to the purity of endmembers, can change the geometry of the dataspace and affect conventional methods used to identify endmembers. Several methods have been developed to identify and extract endmember bundles representing the spectral variability within each endmember class.
Arman Melkumyan +2 more
exaly +2 more sources
Endmember variability in Spectral Mixture Analysis: A review
Remote Sensing of Environment, 2011The composite nature of remotely sensed spectral information often masks diagnostic spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this mixture problem.
Gregory P Asner +2 more
exaly +2 more sources
IEEE Signal Processing Magazine, 2014
Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis.
Alina Zare, K C Ho
exaly +2 more sources
Variable illumination and environmental, atmospheric, and temporal conditions cause the measured spectral signature for a material to vary within hyperspectral imagery. By ignoring these variations, errors are introduced and propagated throughout hyperspectral image analysis.
Alina Zare, K C Ho
exaly +2 more sources
IEEE Transactions on Geoscience and Remote Sensing, 2000
Accuracy of vegetation cover fractions, computed with spectral mixture analysis, may be compromised by variation in canopy structure and biochemistry when a single endmember represents top-of-canopy reflectance. In this article, endmember variability is incorporated into mixture analysis by representing each endmember by a set or bundle of spectra ...
C. Ann Bateson +2 more
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Accuracy of vegetation cover fractions, computed with spectral mixture analysis, may be compromised by variation in canopy structure and biochemistry when a single endmember represents top-of-canopy reflectance. In this article, endmember variability is incorporated into mixture analysis by representing each endmember by a set or bundle of spectra ...
C. Ann Bateson +2 more
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Alternating angle minimization based unmixingwith endmember variability
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Several techniques exist for dealing with spectral variability in hyperspectral unmixing, such as multiple endmember spectral mixture analysis (MESMA) or compositional models. These algorithms are computationally very involved, and often cannot be executed on problems of reasonable size. In this work, we present a new algorithm for solving the unmixing
Rob Heylen +3 more
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Variability of the endmembers in spectral unmixing
2019Spectral unmixing is an inverse problem in hyperspectral imaging that aims at recovering the spectra of the pure constituents of an image (called endmembers), as well as at estimating the proportions of said materials in each pixel (called abundances).
Drumetz, Lucas +2 more
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Quantitative assessment of the different methods addressing the endmember variability
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013Spectral mixture analysis is an important technique to extract desired information from the mixed remotely sensed data. However, current spectral mixture analysis techniques suffered from the endmember variability. Quantitative assessment of SMA techniques with simulated data is critical to understand the influence of endmember variability.
Yuhan Rao +3 more
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Classification Using Unmixing Models in Areas With Substantial Endmember Variability
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018Q1
Edurne Ibarrola-Ulzurrun +4 more
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A Gaussian mixture model representation of endmember variability for spectral unmixing
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2016Endmember variability complicates the problem of spectral unmixing. This variability is typically represented by probability distributions or spectral libraries. The present work describes a new distributional representation based on Gaussian Mixture Models (GMMs).
Yuan Zhou 0004 +2 more
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