Download or read book Exploration of Regularized Covariance Estimates with Analytical Shrinkage Intensity for Producing Invertible Covariance Matrices in High Dimensional Hyperspectral Data written by and published by . This book was released on 2007 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Removing background from hyperspectral scenes is a common step in the process of searching for materials of interest. Some approaches to background subtraction use spectral library data and require invertible covariance matrices for each member of the library. This is challenging because the covariance matrix can be calculated but standard methods for estimating the inverse requires that the data set for each library member have many more spectral measurements than spectral channels, which is rarely the case. An alternative approach is called shrinkage estimation. This method is investigated as an approach to providing an invertible covariance matrix estimate in the case where the number of spectral measurements is less than the number of spectral channels. The approach is an analytic method for arriving at a target matrix and the shrinkage parameter that modify the existing covariance matrix for the data to make it invertible. The theory is discussed to develop different estimates. The resulting estimates are computed and inspected on a set of hyperspectral data. This technique shows some promise for arriving at an invertible covariance estimate for small hyperspectral data sets.