A Density-Based Cluster Kernel RX Algorithm for Hyperspectral Anomaly Detection

Hyperspectral remote sensing image contains abundant spectral information, which has strong ability to distinguish ground objects, thus promoting the development of hyperspectral anomaly detection technology without any prior information. Kernel-RX algorithm uses the kernel function to ably RX algorithm mapped to high-dimensional feature space, which has a strong ability to solve the spectrum inseparable problem in the low dimensional space. However, it also reveals the disadvantages such as large inverse error in ill-conditioned matrix and low efficiency. In order to realize the strong detection performance of KRX algorithm in theory, this paper proposes an improved KRX detection technology based on new clustering algorithm. (1) Due to the strong spectral similarity of spatial neighborhood pixels, the Gram matrix is ill-conditioned, which seriously affects the detection performance of anomalies, so the phenomenon of background error detection is serious. In order to solve the problem of the inverse error of ill-conditioned Gram matrix, the algorithm improves the KRX operator. By decomposing the singular value of Gram matrix and selecting the principal component with larger eigenvalue, the algorithm ensures the inverse accuracy of Gram matrix. In the end, the detection result of the pixel to be measured is expressed by l-2 norm. The experiment shows that the detection effect is improved obviously. (2) Based on the improved KRX, a spatial clustering KRX algorithm is proposed. There is strong a correlation between spatial pixels, which not only leads to the ill-condition of Gram matrix, but also affects the detection efficiency. The experimental results show that combining pixels in the clustercenters can reduce the spatial dimension and improve the computational efficiency. At the same time, the clustering center is given different weight factors according to the size of the cluster, which ensures the detection accuracy. (3) On the other hand, it is difficult to select an appropriate clustering algorithm. Clustering KRX algorithm requires high accuracy and real-time performance. It is found that a new clustering algorithm based on the peak density fast search algorithm has better clustering performance. The Euclidean distance is used to calculate the similarity of any two pixels, and the Local Density and Neighborhood Distance are used to calculate the clustering center. The clustering center is obtained by sorting the results of the Joint Judgement Criterion. The clustering results show that this clustering algorithm is fast and can cluster arbitrary shape distribution, which is very suitable for hyperspectral images with high dimension and complex components, and can be used for repeated clustering with high frequency of anomaly detection. In conclusion, DC-KRX algorithm provides a new idea of hyperspectral anomaly detection based on spatial clustering preprocessing. Finally, the algorithm is compared with the advanced method. The results show our method has a strong detection performance. And, it is found that the detection efficiency of clustering algorithm is improved by more than 30%, which greatly improves the real-time performance of KRX algorithm.