|
Hyperspectral anomaly detection is an active topic in remote sensing application research. Researchers have proposed many detection methods based on spatial differences to detect anomaly targets. However, due to the low spatial resolution of images or human manipulation, the spatial differences of targets in practical applications are not enough to provide reliable support, which reduces the accuracy of anomaly detection. In order to solve this problem and take advantage of high spectral resolution unique to hyperspectral images, this paper proposes the hyperspectral anomaly detection method based on spectral difference extraction. Specifically, spectral derivatives are introduced to extract bands with spectral differences between the tested pixels and surrounding sample pixels to form a combined image, and the corresponding Mahalanobis distance is calculated to obtain suspected anomaly results. Then, the suspected anomaly results are subjected to anomaly assessment on the suspected anomaly part through the kurtosis value to obtain the final detection result. In addition, this paper obtains the corresponding suspected anomaly part in the suspected anomaly results through the corresponding atoms in the background dictionary. Experiments applied on the real datasets show the effectiveness of the proposed method compared with other state-of-the-art methods.
|
