A new approach for discrimination of objects on hyperspectral images, which combines state-of-art image processing methods and multivariate image analysis, is proposed. The basic idea of the approach is to build a joint principal component space for all objects' pixels, detect patterns, pixels from a particular object shared in this space, and use quantitative evaluation of the patterns as the objects' features. The approach was particularly developed for dealing with challenging cases, when objects from different classes have many similar pixels. It has been tested on several real cases and showed very promising results.
Chemometrics and Intelligent Laboratory Systems, 2013, Vol 120, Issue 1, p. 126-135