针对传统密度峰值聚类在波段选择时缺乏信息论角度的相似性度量以及波段数目确定问题，提出基于光谱角-光谱信息散度的自适应密度峰值波段选择方法(SSDPC:Spectral angle mapping and Spectral information divergence Density Peaks Cluster)。该方法将光谱信息散度和光谱角用于高光谱图像密度峰值聚类进行波段选择，取代传统的欧氏距离构建波段相似矩阵。通过构建波段评分策略，有效自动选择出重要的光谱波段子集。在3组高光谱数据集上调用RX(Reed-Xiaoli)算法进行异常检测，在SSDPC的相似性度量方法下，异常检测精度较欧氏距离度量方法分别平均提高1。16％、1。18％和0。07％;在自适应的SSDPC波段选择方法下，异常检测精度相较原始RX算法分别提升6。49％、2。71％和0。05％。结果表明，该算法具有良好的鲁棒性，能提升高光谱图像异常检测的性能并降低其虚警率。
Adaptive Density Peak Clustering Band Selection Method Based on Spectral Angle Mapping and Spectral Information Divergence
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.
peak densitybandselectionspectral angle mappingspectral information divergenceclusteringcenter
万方数据
维普
