Abstract: In order to solve the problem that traditional density peak clustering method without considering similarity of bands in information theory and number of bands in band selection, an adaptive density peak band selection method based on spectral angle mapping and spectral information divergence (SSDPC: Spectral angle mapping and Spectral information divergence Density Peaks Cluster)is proposed. SSDPC combines spectral angle mapping and spectral information divergence for density peak clustering band selection in hyperspectral images, replacing the traditional Euclidean distance to construct a band similarity matrix. By constructing a band scoring strategy, an important subset of spectral bands can be selected automatically and effectively. Using RX(Reed- Xiaoli) algorithm for anomaly detection on three sets of hyper-spectral datasets, the accuracy of anomaly detection is 1. 16% ,1. 18% and 0. 07% higher than that of Euclidean distance measurement under the similarity measure of SSDPC. Under the adaptive SSDPC band selection method, the accuracy of anomaly detection is 6. 49% ,2. 71% and 0. 05% higher than that of the original RX algorithm, respectively. The experimental results show that the SSDPC is robust, can improve the performance of hyper-spectral image anomaly detection and reduce its false alarm rate.

Key words: leak density, bandselection, spectral angle mapping, spectral information divergence, clustering center