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

Anomaly target detection methods for hyperspectral images (HSI) often have the problems of potential anomalies and noise contamination when representing background. Therefore, a spectral-spatial hyperspectral anomaly detection method is proposed in this article, which is based on fractional Fourier transform (FrFT) and saliency weighted collaborative representation. First, hyperspectral pixels are projected to the fractional Fourier domain by the FrFT, which can enhance the capability of the detector to suppress the noise and make anomalies to be more distinctive. Then, a saliency weighted matrix is designed as the regularization matrix referring to context-aware saliency theory and combined with the FrFT-based collaborative representation detector. The saliency-weighted regularization matrix assigns different pixels with different weights by using both spectral and spatial information, which can reduce the influence of the potential anomalous pixels embedded in the background when applying collaborative representation theory. Finally, to further improve the performance of the proposed method, a spectral-spatial detection procedure is employed to calculate final anomaly scores by using both spectral information and spatial information. The proposed method is compared with nine state-of-the-art hyperspectral anomaly detection methods on six HSI datasets, including two synthetic HSI datasets and four real-world HSI datasets. Extensive experimental results illustrate that the proposed method's detection performance outperforms other nine well-known compared methods in terms of area under the receiver operating characteristic (ROC) curve values, visual detection characteristics, ROC curve, and separability.