Results 151 to 160 of about 6,229 (197)

Spectral Unmixing Using Deep Convolutional Encoder-Decoder

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
In this paper, we introduce ‘Unmixing Deep Image Prior’ (UnDIP), a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e. a simplex volume maximization in a subspace of the dataset.
Rasti, B., Koirala, B., Scheunders, P., Ghamisi, P.   +3 more
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Optimal linear spectral unmixing

IEEE Transactions on Geoscience and Remote Sensing, 1999
The optimal estimate of ground cover components of a linearly mixed spectral pixel in remote-sensing imagery is investigated. The problem is formulated as two consecutive constrained least-squares (LS) problems: the first problem concerns the estimation of the end-member spectra (EMS), and the second concerns the estimate, within each mixed pixel, of ...
Y.H. Hu, H.B. Lee, F.L. Scarpace
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Iterative spectral unmixing (ISU)

International Journal of Remote Sensing, 1999
Spectral unmixing techniques strive to find proportions of end-members within a pixel from the observed mixed pixel spectrum and a number of pure end-member spectra of known composition. The outcomes of such analysis are fraction (abundance) images for the selected (pure) end-members and a root mean square (RMS) error estimate representing the ...
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Spectral unmixing using linear unmixing under spatial autocorrelation constraints

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
This paper presents a spectral unmixing approach that is implemented using linear unminxing method by a genetic algorithm. The unmixing is constrained not only by the negativity and sum-to-one of the abundances of endmembers at each pixel but also by the spatial autocorrelation of their abundances among eight neighbor pixels.
Xianfeng Song, Xiaoguang Jiang, Xiaoping Rui   +2 more
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Spectral unmixing using distance geometry

2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011
In this paper, we present a new method for solving the spectral unmixing problem which uses only the spectral distances between the data points and the endmembers. This method is obtained by reformulating every step of the recently developed SPU algorithm entirely in distance geometry, yielding a recursive algorithm based on the geometrical properties ...
Heylen, Rob, Scheunders, Paul
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Particle filter for spectral unmixing

2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, 2011
The paper addresses the problem of identification of fluorescent molecules, or fluorophores, in biological samples obtained from time-resolved fluorescence microscopy. The contribution of this work is an algorithm, based on particle filter optimization, that solves the spectral unmixing problem for more than two fluorophores even when they present ...
Omar Gutierrez-Navarro, Edgar R. Arce-Santana, Daniel U. Campos-Delgado, Martin O. Mendez, Javier A. Jo   +4 more
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Spectral Unmixing via Compressive Sensing

IEEE Transactions on Geoscience and Remote Sensing, 2014
The recently developed theory of compressive sensing (CS) exhibits enormous potentials in signal recovery. In this paper, we investigate its application on spectral unmixing, which appears in hyperspectral data analysis and is usually based on a linear mixture model (LMM) that assumes that a mixed pixel is a linear combination of a set of pure spectral
null Junmin Liu, null Jiangshe Zhang
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Robust Context Dependent Spectral Unmixing

2014 22nd International Conference on Pattern Recognition, 2014
A robust hyper spectral unmixing algorithm that finds multiple sets of end members is introduced. The algorithm, called Robust Context Dependent Spectral Unmixing (RCDSU), combines the advantages of context dependent unmixing and robust clustering. RCDSU adapts the unmixing to different regions, or contexts, of the spectral space. It combines fuzzy and
Hamdi Jenzri, Hichem Frigui, Paul Gader
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Variational methods for spectral unmixing of hyperspectral unmixing

2011
International ...
Eches, Olivier, Dobigeon, Nicolas, Tourneret, Jean-Yves, Snoussi, Hichem   +3 more
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Spectral Unmixing Technique of HSI

2015
Relative to the classification technique, the spectral unmixing (Keshava and Mustard in IEEE Trans Sig Process Mag 19:44–57, 2002) i.e., soft classification technique started late. Although the spectral resolution of the hyperspectral image has been improved greatly, the spatial resolution of the corresponding land object target of the pixel has been ...
Liguo Wang, Chunhui Zhao
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