Unsupervised target detection in hyperspectral images using projection pursuit

In this paper, we present a projection pursuit (PP) approach to target detection. Unlike most of developed target detection algorithms that require statistical models such as linear mixture, the proposed PP is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest. It utilizes a projection index to explore projections of interestingness. For target detection applications in hyperspectral imagery, an interesting structure of an image scene is the one caused by man-made targets in a large unknown background. Such targets can be viewed as anomalies in an image scene due to the fact that their size is relatively small compared to their background surroundings. As a result, detecting small targets in an unknown image scene is reduced to finding the outliers of background distributions. It is known that "skewness," is defined by normalized third moment of the sample distribution, measures the asymmetry of the distribution and "kurtosis" is defined by normalized fourth moment of the sample distribution measures the flatness of the distribution. They both are susceptible to outliers. So, using skewness and kurtosis as a base to design a projection index may be effective for target detection. In order to find an optimal projection index, an evolutionary algorithm is also developed to avoid trapping local optima. The hyperspectral image experiments show that the proposed PP method provides an effective means for target detection.