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Title
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Estimating the number of endmembers in hyperspectral imagery with nearest neighbor distances
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Author
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Abstract
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| We present a new method for estimating the number of end-members present in a hyperspectral data set, based on the scaling behavior of nearest-neighbor distances. We demonstrate the method on artificial data, and show that it has a low dependence on the spectral dimensionality or the size of the data set. Furthermore, the proposed technique gives consistent results over different random instances of the data, indicated by a low standard deviation. On the AVIRIS Cuprite and Indian Pines data set, this technique yields results that are comparable to those obtained via other methods. |
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Language
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English
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Source (journal)
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IEEE International Geoscience and Remote Sensing Symposium proceedings. - [New York]
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Source (book)
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IEEE IGARSS 2012, International Geoscience and Remote Sensing Symposium , Munich, 22-27 July 2012
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Publication
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S.l.
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Institute of Electrical and Electronics Engineers
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2012
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ISBN
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978-1-4673-1158-8
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DOI
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10.1109/IGARSS.2012.6351280
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Volume/pages
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p. 1377-1380
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ISI
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000313189401147
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Full text (Publisher's DOI)
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