Multivariate Statistical Image Processing

Catalog description

This course discusses the digital image processing concepts and algorithms used for the analysis of hyperspectral, multispectral, and multi-channel data in multiple imaging application areas. Concepts are covered at the theoretical and implementation level using current, popular commercial software packages and high-level programming languages to work examples, homework problems and programming assignments. The requisite multivariate statistics will be presented as part of this course as an extension of the univariate statistics that the students have previously been exposed to in the introductory statistics classes. Topics include methods for supervised data classification, clustering algorithms and unsupervised classification, multispectral data transformations, data-redundancy reduction techniques, derivation of non-spectral images features to aid in the classification process, and data fusion for resolution enhancement.