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Volume: 6 | Article ID: art00064
Representing Outliers for Improved Multi-Spectral Data Reduction
  DOI :  10.2352/CGIV.2012.6.1.art00064  Published OnlineJanuary 2012

Large multi-spectral datasets such as those created by multi-spectral images require a lot of data storage. Compression of these data is therefore an important problem. A common approach is to use principal components analysis (PCA) as a way of reducing the data requirements as part of a lossy compression strategy. In this paper, we employ the fast MCD (Minimum Covariance Determinant) algorithm, as a highly robust estimator of multivariate mean and covariance, to detect outlier spectra in a multi-spectral image. We then show that by removing the outliers from the main dataset, the performance of PCA in spectral compression significantly increases. However, since outlier spectra are a part of the image, they cannot simply be ignored. Our strategy is to cluster the outliers into a small number of groups and then compress each group separately using its own cluster-specific PCAderived bases. Overall, we show that significantly better compression can be achieved with this approach.

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Farnaz Agahian, Brian Funt, Seyed Hossein Amirshahi, "Representing Outliers for Improved Multi-Spectral Data Reductionin Proc. IS&T CGIV 2012 6th European Conf. on Colour in Graphics, Imaging, and Vision,  2012,  pp 367 - 371,

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