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Archetypal analysis for endmember bundle extraction considering spectral variability
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018With the development of imaging technology, remote sensing images with a high spatial and spectral resolution have become available and have been used in various applications. Although many endmember extraction algorithms have been proposed for hyperspectral data sets which extract/select the standard endmember spectrum for each existing endmember ...
Mingming Xu 0001 +4 more
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On the effect of variable endmember spectra in the linear mixture model
IEEE Transactions on Geoscience and Remote Sensing, 2006The linear mixture model is frequently used to characterize surface cover over land, to model the reflectance of heterogeneous surfaces, and, by inversion, to estimate fractional cover from a multispectral satellite signal. It is usually assumed that certain parameters of this model, namely the so-called endmember spectra, are fixed, and that the model
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Endmember and band combined model for hyperspectral unmixing with spectral variability
Journal of Applied Remote Sensing, 2020Spectral variability is one of the most limiting factors in hyperspectral unmixing, so it is important to further study the characteristics of spectral variability to improve the accuracy of unmixing. After conducting simulations under varying irradiation conditions, a linear mixed model combining endmember and band is proposed by introducing a band ...
Wuhu Lei +4 more
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Parallel implementation of unmixing algorithm for variable-endmember linear mixture model
2010 IEEE International Geoscience and Remote Sensing Symposium, 2010This study introduces a parallel implementation of unmixing algorithm for a variable-endmember linear mixture model (VELMM). The model had been developed to retrieve leaf area index (LAI) and fraction of vegetation cover (FVC) from remotely sensed surface reflectance.
Naoki Takahashi +3 more
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Unmixing based Change Detection for Hyperspectral Images with Endmember Variability
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018Unmixing based change detection (UBCD) provides subpixel level information on the nature of the changes that occur in a temporal image series, in addition to providing a multi-output change detection map. These advantages have recently carried UBCD to prominence among change detection approaches for hyperspectral images.
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Endmember Variability in hyperspectral image unmixing
Variabilité spectrale dans le démélange d'images hyperspectrales La finesse de la résolution spectrale des images hyperspectrales en télédétection permet une analyse précise de la scène observée, mais leur résolution spatiale est limitée, et un pixel acquis par le capteur est souvent un mélange des contributions de différents matériaux.openaire +1 more source
Endmember variability resolved by pixel purity index in hyperspectral imagery
SPIE Proceedings, 2014Endmember variability presents a great challenge in endmember finding since a true endmember may be contaminated by many unknown factors. This paper develops a pixel purity index (PPI) based approach to resolving this issue. It is known that endmember candidates must have their PPI counts greater than 0.
Yao Li +3 more
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Subpixel Change Detection of Multitemporal Remote Sensed Images Using Variability of Endmembers
IEEE Geoscience and Remote Sensing Letters, 2017Due to the existence of mixed pixels in a remote sensed image, traditional change detection (CD) methods at “full-pixel level” are often unable to provide detailed changed information effectively. A subpixel change detection (SCD) technique can deal with this issue with two steps: soft classification is applied to derive proportional differences from ...
Ke Wu 0004, Qian Du 0001
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Manifold Regularized Sparse Archetype Analysis Considering Endmember Variability
IEEE Geoscience and Remote Sensing Letters, 2023Mingming Xu 0001 +4 more
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Spectral unmixing method considering endmember variability of vegetation
National Remote Sensing Bulletin, 2023WEI Qinhua, LUO Wenfei, TANG Kaifeng
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