Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection

摘要

Hyperspectral anomaly detection (HAD) is of particular interest due to not requiring any previous knowledge on spectra of ground objects. However, developing a fast and accurate detection approach for extracting both sub-pixel and supper-pixel anomalies has been remained challenging in the HAD field. A local and global scene analysis for HAD is investigated in this manuscript based on analytical modelling of the visual attention concept. The proposed method articulated the intrinsic spatial irregularities of the ground objects employing self-information of frequency spike components and adaptive local steering kernels. Also, the detection map obtained using spatial feature analysis is employed as prior information for the learning process of a deep autoencoder network to gain the maximum profit of spectral deviation of anomalous pixels. The potency of the proposed visual attention-based method is investigated on psychological patterns and various hyperspectral data sets. The results confirm the proposed method's potency in detection accuracy and reduce false alarm rates viewpoints compared to some state-of-the-art methods.