Efficient anomaly detection and discrimination for hyperspectral imagery

With the improvement of remote sensing sensor techniques, hyperspectral imagery is widely used today. Hundreds of frequency channels are used to collect radiance from the ground, which results in hundreds of co-registered images. How to process this huge amount of data is a great challenge, especially when no information of the image scene is available. Under this circumstance, anomaly detection becomes more difficult. Several methods are devoted to this problem, such as the well-known RX algorithm which takes advantage of the second-order statistics. In this paper we propose an effective algorithm for anomaly detection and discrimination based on high-order statistics. They include the normalized third central moment referred to as skewness and the normalized fourth central moment referred to as kurtosis, which measure the asymmetry and the flatness of a distribution respectively. The Gaussian distribution is completely determined by the first two statistics and has zero skewness and kurtosis, so these two indices tell us the deviation of a distribution from the Gaussian and are suitable to anomaly detection. The proposed algorithm can be generalized to use any high-order moment. The experimental results with AVIRIS data demonstrate that it can provide comparable detection results with low computational complexity.