Improved RX Algorithm with Global Statistics

Anomaly detection of hyperspectral is a hot issue in the remote sensing field. Anomaly detection algorithms currently proposed can be classified into two class, global algorithm and local algorithm. Global algorithm may lead to miss alarm since the discrimination is not accurate enough. On the contrary, local algorithm may bring about false alarm because of lack of global statistics. An improved RX algorithm integrating local and global statistics is proposed. Firstly K-means algorithm is carried out to cluster the whole image into K class which is determined with a virtual dimension estimation method. Then the improved RX is proposed by integrating the global cluster information and the local statistics. Experiment results show that the improved algorithm can obtain a better detection performance than RX algorithm.