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The effect of noise whitening on methods for determining the intrinsic dimension of a hyperspectral image

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dc.contributor.author Cawse K
dc.contributor.author Robin A
dc.contributor.author Sears M
dc.date.accessioned 2011-07-01T07:25:48Z
dc.date.available 2011-07-01T07:25:48Z
dc.date.issued 2011-06
dc.identifier.citation Cawse, K, Robin, A, and Sears, M. 2011. The effect of noise whitening on methods for determining the intrinsic dimension of a hyperspectral image. WHISPERS 2011, Lisbon, Portugal, 6-9 June 2011, pp 4 en_US
dc.identifier.uri http://hdl.handle.net/10204/5079
dc.description WHISPERS 2011, Lisbon, Portugal, 6-9 June 2011 en_US
dc.description.abstract Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process, and under- or over- estimation of this number may lead to incorrect unmixing for unsupervised methods. It is known that most real images contain noise that is not i.i.d. across bands, and so methods that assume i.i.d. noise are often avoided. However, this problem may be alleviated by implementing a noise whitening procedure as a pre-processing step. In this paper we will investigate one particular noise whitening approach, as well as a noise removal approach, and consider how the application of these methods may improve several methods for determining the intrinsic dimension of an image, including Malinowski’s Empirical Indicator Function [1], Random Matrix Theory [2], and Harsanyi-Farrand-Chang [3]. en_US
dc.language.iso en en_US
dc.relation.ispartofseries Workflow;6695
dc.subject Hyperspectral unmixing en_US
dc.subject Random matrix theory en_US
dc.subject Intrinsic dimension en_US
dc.subject Noise whitening en_US
dc.title The effect of noise whitening on methods for determining the intrinsic dimension of a hyperspectral image en_US
dc.type Conference Presentation en_US


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