Representing outliers for improved multi-spectral data reduction

Video in TIB AV-Portal: Representing outliers for improved multi-spectral data reduction

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Title
Representing outliers for improved multi-spectral data reduction
Alternative Title
Representing outliers for improved multi spectral data reduction
Title of Series
Part Number
22
Number of Parts
31
Author
License
CC Attribution - NoDerivatives 2.0 UK: England & Wales:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Publisher
Release Date
2012
Language
English

Content Metadata

Subject Area
Abstract
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 PCA-derived bases. Overall, we show that significantly better compression can be achieved with this approach.
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  342 ms - page object

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AV-Portal 3.21.3 (19e43a18c8aa08bcbdf3e35b975c18acb737c630)
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