Fengyun Satellites: Achievements and Future

Chinese meteorological satellite, Fengyun (FY) Satellite, has a polar-orbiting series and a geostationary series. Up to now, 5 polar-orbiting (FY-1A/B/C/D and FY-3A) and 5 geostationary (FY-2A/B/C/D/E) satellites were launched. FY data has been being intensively applied not only to meteorological monitoring and prediction but also to many other fields regarding ecology, environment, disaster, space weather and so and. The FY data sharing system, FengyunCast, is now one of the three components of global meteorological satellite information dissemination system, GEONETCast. The first satellite of the new generation polar-orbiting series, FY-3A, was launched on 27 May, 2008, demonstrating the FY polar-orbiting satellite and its application completed a great leap to realize three-dimensional observations and quantitative application. The first of the next generation geostationary series (FY-4) is planned to launch in 2014.      

某遥控终端FTA—FMEA联合故障分析

尝试用ＦＴＡ－ＦＭＥＡ联合２分析法来分析某摇控终端的可靠性。根据系统功能的要求和故障定义确定系统中的一个或多个致命邦联模式，以每种致命故障模式为顶事件，并且作ＦＴＡ分析，找出关键件，然后作ＦＭＥＡ分析。     

Track detection of high-velocity resident space objects in Low Earth Orbit

The world’s economy has become heavily dependent on the services provided by satellites. With the exponential increase in satellite launches, the population of defunct or inactive hardware in space has grown substantially. This is especially true in sensitive orbits such as the Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. These objects, collectively known as orbital debris, can reach speeds of up to 28 000km h^?1 in LEO. At these orbital speeds, even the smallest of objects can pose a considerable threat to operational satellites or astronauts. This makes the monitoring, and detection, of these objects of the utmost importance. This work describes the latest detection strategy used in one of Europe’s largest Space Situational Awareness (SSA) installation; the BIstatic RAdar for LEo Survey (BIRALES) space debris radar. We present a novel bottom-up approach that makes use of single-linkage clustering to identify faint radar streaks in spectrogram data. Tests on synthetic data have shown that the detection strategy presented in this study obtains a higher detection rate when it is compared against existing methods. Unlike other approaches, this detection strategy, using the Multi-beam streak detection strategy (MSDS) algorithm, was still able to recall 90% of the track information at an Signal-to-Noise Ratio (SNR) of 2dB.     

GroupNet: Learning to group corner for object detection in remote sensing imagery

Due to the attractive potential in avoiding the elaborate definition of anchor attributes,anchor-free-based deep learning approaches are promising for object detection in remote sensing imagery. Corner Net is one of the most representative methods in anchor-free-based deep learning approaches. However, it can be observed distinctly from the visual inspection that the Corner Net is limited in grouping keypoints, which significantly impacts the detection performance. To address the above problem, ...     

三维编织机锭子,轨道及锭槽结构尺寸的选择

分析了三维四步法编织机锭子运动的特点，提出了锭子与锭槽尺寸公差的选择原则；讨论了各种误差存在时，锭子可能窜入相邻轨道锭槽的情况，提出了防止锭子卡死或发生其它不良情况的结构措施，并举例进行了结构尺寸的选择。     

SRS-Net: Training object detectors from scratch for remote sensing images without pretraining

Most of the current object detection algorithms use pretrained models that are trained on ImageNet and then fine-tuned in the network, which can achieve good performance in terms of general object detectors. However, in the field of remote sensing image object detection, as pretrained models are significantly different from remote sensing data, it is meaningful to explore a train-from-scratch technique for remote sensing images. This paper proposes an object detection framework trained from scratch, SRS-Net, and describes the design of a densely connected backbone network to provide integrated hidden layer supervision for the convolution module. Then, two necessary improvement principles are proposed: studying the role of normalization in the network structure, and improving data augmentation methods for remote sensing images. To evaluate the proposed framework, we performed many ablation experiments on the DIOR, DOTA, and AS datasets. The results show that whether using the improved backbone network, the normalization method or training data enhancement strategy, the performance of the object detection network trained from scratch increased. These principles compensate for the lack of pretrained models. Furthermore, we found that SRS-Net could achieve similar to or slightly better performance than baseline methods, and surpassed most advanced general detectors.     

两种同类型胶片基础特性及检测应用比对

文摘针对如何在液体火箭发动机产品射线检测过程中,根据具体情况选择合适的射线胶片,达到既能保证射线检测图像质量又能提高检测效率减少检测成本的目的,本文通过对进口富士IX100型胶片和国产乐凯牌L7型胶片的特性和质量稳定性进行工艺对比试验。结果表明,L7型胶片本底灰雾度稍大于IX100型胶片,灵敏度逊于IX100型胶片,宽容度高于IX100型;但两者均属于GB/T19348.1—2003规定的T3范畴胶片,综合性能比较接近;L7型胶片适用于发动机上具有变截面的铝铸件、机加余量不大的钢铸件、钛铸件及大部分熔焊缝的射线检测。     

基于条件生成对抗模型的遥感卫星影像重建技术研究

为了快速侦察未知区域的地貌信息，遥感卫星可对特定区域进行扫描以获取遥感卫星影像。当卫星经过国外未知区域时，部分卫星无法针对某特定区域进行长时间的驻留扫描，本文提出一种基于条件生成对抗网络模型（Conditional Generative Adversarial Network，CGAN）进行网络训练，前期将某方法获取的区域轮廓地形信息作为CGAN网络的生成网络和鉴别网络中的条件约束信息，通过网络生成器与判别器在训练过程中互相博弈产生特定的输出集，有效地实现由单张电子轮廓图像到对应卫星遥感图像的端到端的非线性映射。本文通过原真实卫星遥感图像与生成卫星遥感图像进行四种对比误差计算，平均误差、均方误差与结构相似度均高于99%，峰值信噪比高于30 dB，生成的图像与原图像之间具备高相似度，实现了在获取坐标定位轮廓信息的先验条件下，对特定区域进行遥感卫星影像内容重建技术。     

Exploring the effect of COVID-19 pandemic lockdowns on urban cooling: A tale of three cities

COVID-19 pandemic has had a major impact on our society, environment and public health, in both positive and negative ways. The main aim of this study is to monitor the effect of COVID-19 pandemic lockdowns on urban cooling. To do so, satellite images of Landsat 8 for Milan and Rome in Italy, and Wuhan in China were used to look at pre-lockdown and during the lockdown. First, the surface biophysical characteristics for the pre-lockdown and within-lockdown dates of COVID-19 were calculated. Then, the land surface temperature (LST) retrieved from Landsat thermal data was normalized based on cold pixels LST and statistical parameters of normalized LST (NLST) were calculated. Thereafter, the correlation coefficient (r) between the NLST and index-based built-up index (IBI) was estimated. Finally, the surface urban heat island intensity (SUHII) of different cities on the lockdown and pre-lockdown periods was compared with each other. The mean NLST of built-up lands in Milan (from 7.71 °C to 2.32 °C), Rome (from 5.05 °C to 3.54 °C) and Wuhan (from 3.57 °C to 1.77 °C) decreased during the lockdown dates compared to pre-lockdown dates. The r (absolute value) between NLST and IBI for Milan, Rome and Wuhan decreased from 0.43, 0.41 and 0.16 in the pre-lockdown dates to 0.25, 0.24, and 0.12 during lockdown dates respectively, which shows a large decrease for all cities. Analysis of SUHI for these cities showed that SUHII during the lockdown dates compared to pre-lockdown dates decreased by 0.89 °C, 1.78 °C, and 1.07 °C respectively. The results indicated a high and substantial impact of anthropogenic activities and anthropogenic heat flux (AHF) on the SUHI due to the substantial reduction of huge anthropogenic pressure in cities. Our conclusions draw attention to the contribution of COVID-19 lockdowns (reducing the anthropogenic activities) to creating cooler cities.     

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。