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Title
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Alternating angle minimization based unmixing with endmember variability
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Author
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Abstract
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| Several techniques exist for dealing with spectral variability in hyperspectral unmixing, such as multiple end member 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 problem when spectral variability is present. The algorithm uses a library-based approach to describe the variability present in each class, and executes an alternating optimization with respect to these libraries. The optimization problem itself is constructed as an angle minimization problem by exploiting the geometrical interpretation of the unmixing problem. This results in an algorithm which yields almost identical results as MESMA, but is computationally much more favorable. |
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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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36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 10-15, 2016, Beijing, PEOPLES R CHINA
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Publication
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New york
:
Ieee
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2016
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ISSN
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2153-6996
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ISBN
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978-1-5090-3332-4
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978-1-5090-3332-4
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DOI
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10.1109/IGARSS.2016.7730819
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Volume/pages
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(2016)
, p. 6974-6977
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ISI
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000388114606195
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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