Hyperspectral Data Processing: Algorithm Design and Analysis

16.3 Correlation-Weighted Hyperspectral Measures for Target Discrimanition and Identification

The signature vector-based spectral measures described in Section 16.2 calculate the spectral similarity value between a pair of two signature vectors using only the spectral information provided by L bands within these two signature vectors. So, if a material signature vector is mixed by other substances, the spectral characteristics of the signature vector to be processed do not necessarily characterize the spectral properties of the material signature vector it represents. This often occurs in real applications when a material signature vector is either mixed with other signature vectors such as background signatures or embedded in a single signature vector as a subsample target. In both cases, using a signature vector-based spectral measure to measure material similarity is generally not effective. In order to resolve this dilemma, signature vector-based hyeprspectral measures are extended to correlation-weighted hyperspectral measures that can be categorized into two classes. One comprises of hyperspectral measures that introduce a priori sample spectral correlation into signature vector-based spectral measure so as to improve discrimination performance in spectral similarity. The other is made up of hyperspectral measures weighted by a posteriori sample spectral correlation to do what a priori sample spectral correlation does.

16.3.1 Hyperspectral Measures Weighted by A Priori Correlation ...