SerDes器件在遥感相机系统中的应用

基于当前遥感相机谱段数量的不断增加、分辨率的不断提升等方面造成的数据传输问题,分析了目前广泛应用的并行数据传输系统所面临的技术瓶颈,提出采用串行发送/解串(SerDes)方式加以替代的方案,通过对比分析,指出采用该方式传输数据的优点,进而阐述文章所采用的串行发送/解串芯片-TLK2711的工作原理、传输协议等,最终通过试验证明了其在遥感相机系统中应用的可行性,为后续遥感相机研制提供参考。     

尖楔前体飞行器FADS系统驻点压力对神经网络算法精度的影响

针对尖楔前体（类乘波体）飞行器用嵌入式大气数据传感（FADS）系统存在建模困难及解算模型精度低的问题,首先采用BP神经网络建模代替传统的FADS系统空气动力学建模的方法,建立含有双隐含层的四层神经网络模型,然后通过合理选择网络结构参数及训练验证,对FADS系统的攻角进行解算,最后对驻点压力对算法精度的影响进行研究。结果表明,本文建立的含有双隐含层的四层神经网络模型精度较高,攻角测试误差小于 0.25°; 驻点压力是否作为输入参数对FADS系统神经网络算法求解精度影响较大,攻角测试误差相差达0.1°。在飞行器前缘半径允许的情况下,应尽量得到驻点压力用于解算攻角,提高解算精度。     

一种基于压缩感知的高分辨率宽测绘带合成孔径雷达成像算法

针对高分辨率宽测绘带合成孔径雷达（High Resolution Wide Swath Synthetic Aperture Radar, HRWS SAR）在俯仰向波束形成受地面目标高程影响造成增益损失以及在方位向非均匀采样造成模糊的问题,文中提出了一种基于压缩感知（Compressed Sensing, CS）技术的HRWS SAR成像算法。根据SAR系统和平台参数建立精确的观测模型后,通过求解优化问题直接准确地估计出了在地面高程变化影响下的目标来波方向（Direction of Arrival, DOA）并重建了非均匀采样下的方位向观测场景,从而实现了HRWS SAR在俯仰向和方位向的非模糊成像。仿真结果表明了本文算法的有效性。     

基于优先级和率失真模型的CCSDS-IDC码率分配算法

为了解决遥感图像采集与空间网络传输能力之间的矛盾,迫切需要具有码率控制功能的图像压缩算法。文章面向军事目标监测领域图像目标区域局部高质量需求,针对CCSDS-IDC（TheConsultativeCommitteeforSpaceDataSystems ImageDataCompressionStandard）图像压缩标准,提出了一种码率分配算法。新算法改进了原算法的分段方式,设置了段优先级,并根据优先段覆盖的比例及率失真模型设计了码率分配方案。结果表明新算法在局部峰值信噪比和局部视觉效果上较原算法有明显的提高,当优先段覆盖率低于压缩比倒数时,新算法的整体峰值信噪比与原算法接近。     

遥感卫星高速数传信息流设计

随着卫星遥感技术水平的提高,遥感数据的类型和数据量快速增加。为适应多类型、高速率遥感数据传输的复杂需求,对数传信息流进行了顶层设计,定义了数传与遥感系统数据接口以及数传帧格式,对遥感数据传输所需码速率进行了分类计算,为设计固定的下行数传码速率提供了依据。进而针对不同类型的遥感数据提出了基于分组优先级虚拟信道动态调度策略的数传信息流设计方案,确保不同类型遥感数据的传输满足不同的应用需求。对高速遥感数据确保满足较低的缓存容量需求,对低速遥感数据确保满足实时性传输需求。采用动态仿真技术对数传信息流设计方案进行了试验验证。设计方案可为后续新一代遥感卫星数传系统设计提供参考。     

遥感影像中电磁特性数据挖掘

从数据挖掘和遥感信息提取的概念出发,提出了遥感影像中电磁特性数据挖掘的思想,从电子信息装备试验对电磁特性数据的需求和SAR影像数据处理的需求出发,重点完成了基于电磁特性的地物分类和面向对象的遥感影像电磁特性数据挖掘,其中面向对象的遥感影像电磁特性数据挖掘包含遥感影像准备与数据预处理、影像分割、对象的层次结构、规则建立、电磁特性数据获取、信息提取等六个部分,最后把理论方法进行了实践,为大批量获取遥感影像中的电磁特性数据提供了方法支持。     

Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

三维物体表面测量光传感技术

建立了小孔成像摄相机模型,提出了将投影仪看作主动相机的隐函数线性模型.采用了四幅相移光栅条纹编解码技术进行数据匹配,提出了基于质量图的枝切截断算法,处理解码过程中由于图像噪声、孔洞、阴影等导致的相位误差.最后,搭建了实验原型系统,选取了实物模型进行测量试验.实验结果表明,该3-D传感系统工作稳定、精度高,在150mm×200mm的测量范围,精度误差约±0.04 mm.     

Earth remote sensing as an effective tool for the development of advanced innovative educational technologies

Current educational system is facing a contradiction between the fundamentality of engineering education and the necessity of applied learning extension, which requires new methods of training to combine both academic and practical knowledge in balance. As a result there are a number of innovations being developed and implemented into the process of education aimed at optimizing the quality of the entire educational system. Among a wide range of innovative educational technologies there is an especially important subset of educational technologies which involve learning through hands-on scientific and technical projects. The purpose of this paper is to describe the implementation of educational technologies based on small satellites development as well as the usage of Earth remote sensing data acquired from these satellites. The increase in public attention to the education through Earth remote sensing is based on the concern that although there is a great progress in the development of new methods of Earth imagery and remote sensing data acquisition there is still a big question remaining open on practical applications of this kind of data. It is important to develop the new way of thinking for the new generation of people so they understand that they are the masters of their own planet and they are responsible for its state. They should desire and should be able to use a powerful set of tools based on modern and perspective Earth remote sensing. For example NASA sponsors “Classroom of the Future” project. The Universities Space Research Association in United States provides a mechanism through which US universities can cooperate effectively with one another, with the government, and with other organizations to further space science and technology, and to promote education in these areas. It also aims at understanding the Earth as a system and promoting the role of humankind in the destiny of their own planet. The Association has founded a Journal of Earth System Science Education. Authors describe an effective model of educational technology developed in the Center for Earth Remote Sensing of Bauman Moscow State Technical University and based on scientific and educational organizations integration in the field of applied studies. The paper also presents how students are being trained to acquire and process satellite imagery data from Terra and Aqua satellites. It also reveals the results of space monitoring for Russia's ecologically complex regions conducted by Bauman Moscow State Technical University students in cooperation with specialists from the Laboratory for Aerospace Methods of Moscow State University named after M. Lomonosov.