A Spectral-Spatial Method Based on Fractional Fourier Transform and Collaborative Representation for Hyperspectral Anomaly Detection

Anomaly detection (AD) is one of the most important tasks in hyperspectral image (HSI) processing. Most of the traditional AD methods fail to take the advantage of rich spatial information of HSIs and suffer the problem of noise contamination. To solve these problems, we propose a fractional Fourier transform and collaborative representation-based spectral-spatial hyperspectral anomaly detector (SSFrFTCRD). Different from the previous work, fractional Fourier transform (FrFT) is associated with collaborative representation detector (CRD) in the proposed method. FrFT can transfer HSI pixels into a FrFT domain, which can suppress noise and improve the discrimination between background and anomalies. By taking advantage of the CRD, the SSFrFTCRD can adaptively estimate the background through a sliding dual window without assuming its distribution. Furthermore, both spectral and spatial information are utilized to enhance the performance of the proposed detector. Experiments show that the proposed anomaly detector SSFrFTCRD can achieve superior results compared with the other state-of-the-art methods.