Comparison of electron density measurements from CSES and Swarm satellites with GNSS ionospheric tomography data

Electron density measurements obtained from China Seismo‐Electromagnetic Satellite (CSES) and Swarm-B can play an increasingly important role in the study of ionosphere above F2 peak height. This study presented a comprehensive comparison of electron density products obtained from Langmuir probe mounted on CSES and Swarm-B with ionospheric tomography for a whole year period of 2019. CSES was fully compared with Swarm-B on a global scale, including both absolute and relative differences, and a new index called NFI was developed to better quantify the similarity between two latitudinal profiles of electron density. CSES and Swarm-B were then compared with tomography respectively in four regions, roughly located in America, Europe, Australia and China. Results indicated that CSES data are consistent with Swarm-B, as NFI values exceed 0.6 for most of the analyses. Tomography and Swarm-B were found to have a good agreement as their biases are less than 0.2 × 10⁵ el/cm³ in general. For the comparison between CSES and tomography, the bias increased to around 0.6 × 10⁵ el/cm³ but the standard deviation changed slightly, validating the underestimation of electron density by CSES. The spatiotemporal comparisons of CSES and Swarm-B with tomography showed that: 1) the differences in electron density were relatively low in middle latitudes and increased rapidly in the regions of equatorial ionization anomaly; 2) Swarm-B has a better consistent with tomography than CSES, but both are capable of detecting ionosphere anomalies such as midlatitude arcs; and 3) CSES and Swarm-B both can capture the seasonal changes of electron density, while their values are basically smaller than those from tomography in Spring and Summer months.     

遥感场景分类的批处理协方差神经网络方法

针对卫星遥感图像场景分类数据集中存在的局部区域特征异常问题，提出一种采用批处理协方差层的神经网络(CovNN)模型进行遥感场景分类的方法。该方法通过计算全输入通道的局部区域均值实现一种3D批处理协方差算法，能够有效消除局部区域均值的影响，从而更好地处理局部光照过强和局部区域存在无关特征的问题。将其应用于存在局部光照异常和局部无关特征问题的卫星采集AID数据集和NWPU RESISC45数据集中，实验表明CovNN在两个数据集上均取得了超过现有卷积神经网络(CNN)的召回率，可有效降低图像局部区域特征异常的不利影响。     

基于时空数据模型的障碍物数据集数据查询与应用研究

障碍物数据集是国际民航组织在航空情报管理（AIM）体系下提出的一种基于航空信息交换模型（AIXM）规范的航空情报数据集，该数据集中障碍物的查询将直接影响机场净空评估与飞行程序设计。在分析障碍物数据集的时间与空间属性基础上，利用时空数据模型与航空信息交换模型规范，提出一种基于时间点和空间位置的障碍物数据集查询方法， 用于解决障碍物数据集的查询问题；构建障碍物查询与可视化系统，并通过设计随机实验和实例应用对该方法的可行性和可靠性进行验证。结果表明：该障碍物数据集查询方法能够提取出对机场净空与飞行程序设计有影响的障碍物，增强了飞行程序设计人员对机场障碍物分布的情景意识。