Subpixel target detection in hyperspectral data using higher order statistics source separation algorithms

Hyperspectral data is modeled as an unknown mixture of original features (such as the materials present in the scene). The goal is to find the unmixing matrix and to perform the inversion in order to recover them. Unlike first and second order techniques (such as PCA), higher order statistics (HOS) methods assume the data has nongaussian behavior are able to represent much subtle differences among the original features. The HOS algorithms transform the data such that the result components are uncorrelated and their nongaussianity is maximized (the resulting components are statistical independent). Subpixel targets in a natural background can be seen as anomalies of the image scene. They expose a strong nongaussian behavior and correspond to independent components leading to their detection when HOS techniques are employed. The methods presented in this paper start by preprocessing the hyperspectral image through centering and sphering. The resulting bands are transformed using gradient-based optimization on the HOS measure. Next, the data are reduced through a selection of the components associated with small targets using the changes of the slope in the scree graph of the non-Gaussianity values. The targets are filtered using histogram-based analysis. The end result is a map of the pixels associated with small targets.