Anomalies detection in hyperspectral imagery using projection pursuit algorithm

Hyperspectral imagery provides detailed spectral information on the observed scene which enhances detection possibility, in particular for subpixel targets. In this context, we have developed and compared several anomaly detection algorithms based on a projection pursuit approach. The projection pursuit is performed either on the ACP or on the MNF (Minimum Noise Fraction) components. Depending on the method, the best axes of the eigenvectors basis are directly selected, or a genetic algorithm is used in order to optimize the projections. Two projection index (PI) have been tested : the kurtosis and the skewness. These different approaches have been tested on Aviris and Hymap hyperspectral images, in which subpixel targets have been included by simulation. The proportion of target in pixels varies from 50% to 10% of the surface. The results are presented and discussed. The performance of our detection algorithm is very satisfactory for target surfaces until 10% of the pixel.