Endmember generation by projection pursuit

Projection pursuit (PP) is an interesting concept, which has been found in many applications. It uses a so-called projection index (PI) as a criterion to seek directions that may lead to interesting findings for data analysts. Unlike the principal components analysis (PCA), which uses variance as a measure to find directions that maximizes data variances, the PI used by the PP finds interesting directions that can be characterized by statistics higher than variance. As a result, the PCA is generally considered as a special case of PP with the PI particularly specified by the variance. Recently, a PP-based approach was developed by Ifarraguerri and Chang for multispectral/hyperspectral image analysis. This paper revisits their approach and investigates its application in endmember generation where endmembers can be extracted from a sequence of projections generated by PP.