Shearlet transform based anomaly detection for hyperspectral image

Hyperspectral image (HI) contains data in hundreds of narrow contiguous spectral bands, thus it provides a powerful means to distinguish different materials on the basis of their unique spectral signatures. Anomaly detection (AD) is one key part of its application. The shearlet transform (ST) is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks, which can effectively captures smooth contours that are the dominant feature in natural image. In this paper, ST is used in AD for the HI. Firstly, the raw HI data is decomposed into several directional subband at multiple-scale via ST. Thus, the background signal would be reduced in each subband. Secondly, the fourth partial differential equation method is adopted to modify the coefficient of each sub-band, which is for background suppression and anomaly signal enhancement. Thirdly, the kernel-based RX algorithm is adopted to detect the anomaly in each sub-band. Finally, the anomaly signal image is achieved by reconstructing the image with all modified sub-band. Several experiments with a HYDICE data are fulfilled to validate the performance of the proposed method. Compared with the original RX algorithm, experimental results show that the proposed algorithm has better detection performance and lower false alarm probability.