An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years, the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper, which is based on the Hyperspectral Features (HFs) of hypersonic targets covered with plasma sheath during high-speed flight. First, a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs. Then, the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar. Finally, an unsupervised classification method based on the Density Peak Clustering (DPC) for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy. The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set. The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient.     

珠海一号高光谱卫星辐射精度真实性检验研究

“珠海一号（OHS）”高光谱商业遥感卫星数据在国民经济建设中发挥着越来越重要的作用。当前，针对商业遥感卫星数据质量精度的验证和评价研究成果较少。本文基于地面实测数据，对OHS卫星高光谱数据的辐射精度进行了真实性检验和定量分析。结果表明：OHS卫星表观辐亮度与实测结果具有较好的线性相关关系，相关系数R²大于0.7。与实测结果相比，OHS卫星各通道表观辐亮度值整体偏低，但两者光谱曲线形态基本一致。随着时间的推移，OHS传感器性能可能存在衰减，不同通道的衰减程度不一；在31个通道中，与实测值之间的相对差异在20%以内的通道数为9个；在20%~30%之间为14个，30%~40%为8个。