Anomaly Detection Algorithm Based on Nonsubsampled Pyramid Decomposition and Kernel Unsharp Masking for Hyperspectral Image

An anomaly detection algorithm for hyperspectral images based on nonsubsampled Pyramid decomposition (NSPD) was proposed. Both spatial and spectral information have been used to locate and detect the anomaly under the condition of no prior knowledge about the anomaly and the background. Firstly, the hyper-spectral images was decomposed into a series of different scale sub-bands using NSPD; and then using the correlation of neighborhood coefficient of different scale space in a wave-band, the background data was optimally predicted by reducing the anomalous data using the improved kernel unsharp masking filter in different scale of each sub-band. Finally the anomaly targets could be detected by using the RX operator in the feature space. Numerical experiments were conducted on real and synthesized hyperspectral data to validate the effectiveness of the proposed algorithm. Compared with the classical RX algorithm, several experimental results show that the proposed algorithm has better detection performance and lower false alarm probability.