Representing Outliers for Improved Multi-Spectral Data Reduction

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Final version published as: 

Agahian, F., Funt, B., and Amirshahi, S.H., "Representing Outliers for Improved Multi-Spectral Data Reduction," Proc. CGIV2012 IS&T Sixth European Conf. on Colour in Graphics, Imaging and Vision, Amsterdam, May 2012.

Date created: 

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.


Presented at the CGIV 2012 IS&T Sixth European Conference on Colour in Graphics, Imaging and Vision, May 2012.

Document type: 
Conference presentation
Rights remain with the authors.