GNSS多天线基线网单历元模糊度同步解算法

提出多天线基线网单历元模糊度同步解算法，其具体实现步骤为：1）根据宽巷组合模糊度易于固定的优点，采用附加已知基线长度约束法同步解算各基线的宽巷模糊度，得到dm~cm级精度的近似基线分量；2）将解算得到的各近似基线分量作为约束，同步解算各基线的基频模糊度，以获取mm级精度的基线矢量。该方法的关键在于检验各历元宽巷模糊度解算的正确性，以获取可靠的近似基线分量，为解算各基线的基频模糊度提供准确的基线先验信息。由于动态情况下各历元观测信息比较少，单纯依赖ratio检验不可靠，提出结合基线误差、单位权中误差、基线网模糊度闭合环及ratio值等对多组宽巷模糊度进行检验，避免ratio值设置不当导致模糊度检验中发生纳伪和弃真问题。实测数据结果证明，该处理方法使得模糊度解算的成功率提高1%~2%，可以获取移动平台更丰富的导航信息，提高其服务能力。     

A-FPN算法及其在遥感图像船舶检测中的应用

光学遥感图像船舶检测主要面临两个挑战:光学遥感图像背景复杂,船舶检测易受海浪、云雾及陆地建筑等多方面干扰;遥感图像分辨率低,船舶目标小,对于其分类与定位带来很大困难;针对上述问题,在FPN的基础上,提出一种融入显著性特征的卷积神经网络模型A-FPN (Attention-Based Feature Pyramid Networks)。首先,利用卷积提取图像特征金字塔;然后,利用顶层金字塔逐级构建显著特征层,抑制背景信息,通过金字塔顶层的细粒度特征提高浅层特征的表达能力,构建自上而下的多级显著特征映射结构;最后利用Softmax分类器进行多层级船舶检测。A-FPN模型利用显著性机制引导不同感受下的特征进行融合,提高了模型的分辨能力,对遥感图像处理领域具有重要应用价值。实验阶段,利用公开的遥感目标检测数据集NWPU VHR-10中的船舶样本进行测试,准确率为92.8%,表明A-FPN模型适用于遥感图像船舶检测。     

PTP协议在广电时间同步网中的应用研究

随着广电行业的飞速发展,以串行数字接口(SDI)基带信号为基础技术架构的传统电视信号已经很难满足未来技术发展和业务扩展的需求．得益于现代通信技术的迅速发展以及即将到来的万物互联的5G时代,广电行业也将采用IP化技术来构建新的网络系统．处在向IP化发展的过渡期,广电IP化目前还存在一些亟待解决的问题．为了解决广电系统网络中需要更高精度的同步信号来完成时间同步的问题,本文利用精确时间协议（PTP）时间同步技术在专用以太网中完成时间同步组网,搭建了测试环境并对测试数据进行了分析．测试结果表明,相较于传统的网络时间协议（NTP）时间同步技术,输出PTP信号的准确度能够达到亚微秒量级,完全满足广电行业IP化数字电视系统时间同步信号的高精度需求．      

Evaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification,visual interpretation and machine learning approaches

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected.     

国产高分辨率遥感影像农村公路核查方法及其应用

传统的农村公路核查需要人工实地抽查或通过GNSS设备进行信息采集验核，存在成本高、效率低等问题。遥感影像具有成像范围广、时效性高、成本低、能客观反映现实情况等优点。相比于传统方法，将遥感影像引入农村公路核查，能客观、准确、高效地对农村公路相关信息进行核查。本文基于国产高分辨率遥感影像，结合农村公路遥感核查业务，采用遥感影像道路提取算法，设计并实现了一种农村公路核查方法。将本方法应用于某中部省份农村公路遥感核查业务，实际应用表明该方法能有效提高现有农村公路遥感核查的工作效率。     

路网约束条件下热力规则设计与试验

热力图是一种能直观准确展示空间观测值的有效工具，在多个领域具有广泛应用。本文在分析设定不同道路层权重、道路技术等级分类的基础上，以反距离权重、直方图均衡化、密度补偿、参数多次迭代等方法，研究构建了以热力方式展示道路网分布及发达程度的相关规则体系。热力规则通过道路赋权、路线曲面化、密度协调、图面综合等多套规则的有机结合，实现了道路网总体发达水平信息的提取与展示，并以全球地理信息资源建设项目路网成果中的亚洲和非洲部分国家数据为例，进行了信息提取与热力分布试验。     

平衡剖面的制作流程及其地质意义

平衡剖面技术是地质思维和计算机技术的结晶,使对断层构造的研究提高到定量阶段,其依据是在垂直构造走向的剖面上,地层长度和面积(2D)或体积(3D)是均衡的。在此原理基础上利用数学手段对盆地的构造发育史进行正演和反演模拟,直观地再现地下构造的原始几何形态,迅速提供地震剖面的构造解释方案,并对解释结果进行检验(不平衡的剖面其解释一般有问题),为深刻认识构造发育史、分析油气运移及聚集规律提供依据,提高了工作效率。其结果也为盆地模拟、油藏模拟、定量计算构造伸缩量等地质研究打下了坚实的基础[1]。     

空间信息网络服务中的数据安全策愀

以地理信息网络服务的安全策略为研究对象，根据我国空间数据生产到应用整个流程保密管理规定，研究地理空间数据在存储、分发、传输、表现以及与其他系统集成应用阶段上的安全策略，解决地理空间数据共享与空间数据安全之间的矛盾。     

AN EVALUATION OF SAS/GRAPH SOFTWARE FOR COMPUTER CARTOGRAPHY1

The SAS® computer software system, widely used and respected for its capabilities in statistical analysis and data base management, now includes a new set of graphic and cartographic procedures called SAS GRAPH?. We have used these cartographic procedures in research on mapping ethno-cultural census data from metropolitan areas in Ontario and in undergraduate and graduate courses in computer cartography. On the basis of that experience, we describe and evaluate SAS/GRAPH'S cartographic capabilities and illustrate with maps drawn by various devices.     

单片机控制的电子显示屏

本文介绍了由８０３１单片机控制的ＬＥＤ发光二极管阵列构成的电子显示屏显示原理、显示屏数据锁存、驱动接口电路、键盘接口电路、字模存放电路及软件框图。