Hyperspectral Data Processing: Algorithm Design and Analysis

16.2 Signature Vector-Based Hyperspectral Measures for Target Discrimanition and Identification

Spectral characteristics provide important and crucial features in material identification, discrimination, detection, and classification. Many spectral similarity measures have been developed and can be used for this purpose such as SAM (Schwengerdt, 1997), SID (Chang, 2000, 2003a, Chapter 2), Euclidean distance (ED), and many others (Chang, 2003a). When there is no prior target class information available, these measures are performed on a single signature vector basis to measure spectral variability between two signature vectors, in which case they are generally used for signature discrimination and identification, but not used for classification. Furthermore, they are effective only if the spectral signature vectors to be compared are true signatures of the materials that they really represent the signature vectors. However, this idealistic case is generally not true in many real applications where many factors may contaminate and corrupt spectral signature vectors to be identified. One scenario is mixed signature discrimination and identification where a spectral signature vector is mixed with a number of signature vectors resident in the signature vector. Another is subsample target discrimination and identification where the target to be identified is embedded in a single signature vector and its spectral signature vector is clearly mixed with other signature vectors that are ...