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Title
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Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain
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Author
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Abstract
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| In the linear mixing model, many techniques for endmember extraction are based on the assumption that pure pixels exist in the data, and form the extremes of a simplex embedded in the data cloud. These endmembers can then be obtained by geometrical approaches, such as looking for the largest sim- plex, or by maximal orthogonal subspace projections. Also obtaining the abundances of each pixel with respect to these endmembers can be completely written in geometrical terms. While these geometrical algorithms assume Euclidean geom- etry, it has been shown that using different metrics can offer certain benefits, such as dealing with nonlinear mixing effects by using geodesic or kernel distances, or dealing with correla- tions and colored noise by using Mahalanobis metrics. In this paper, we demonstrate how a linear unmixing chain based on maximal orthogonal subspace projections and simplex pro- jection can be written in terms of distance geometry, so that other metrics can be easily employed. This yields a very flex- ible processing chain: by using other metrics, the same un- mixing methodology can be used to deal with a wide range of unmixing models and scenarios. As an example, metrics are provided for dealing with intimate mixtures, nonlinear di- mensionality reduction, and colored noise. |
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Language
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English
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Source (journal)
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Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing : [proceedings]. - Piscataway, NJ
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Source (book)
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IEEE-Whispers 2014 : Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse, June 24-27, 2014
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Publication
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S.l.
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IEEE
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2016
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ISSN
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2158-6268
2158-6268
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ISBN
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978-1-4673-9012-5
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Volume/pages
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p. 1-4
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ISI
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000428980100067
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