基于北斗短报文的震源船跟踪系统设计与实现

通过在震源船上部署3套GPS接收机,并对3个点的位置信息进行汇总和压缩,利用北斗短报文将压缩后的位置信息推送到陆海联测指挥部,实现对震源船航速、航向的实时监控.根据3个点位信息计算的实时震源船航向,有助于准确把握震源船在进行固定点悬停激发时的航向,克服了以往使用前后两个时刻推算的平均航向代替实时航向等问题.使用我国自主知识产权的北斗短报文进行通讯,大大提高了系统的安全性、可靠性和稳定性;同时陆海联测指挥部可以实时查看震源船是否按事先设定的测线、事先设定的速度和航向进行作业,有效保障了监控平台对震源船的监控,进一步提升安全生产的风险控制能力.     

组合粗差探测的MHSS ARAIM算法

针对目前MHSS ARAIM (multiple hypothesis solution separation advanced receiver autonomous integrity monitoring)算法存在的抗差能力弱、计算子集过多、计算量过大等不足,提出一种组合粗差探测的MHSS ARAIM算法。该算法先用粗差探测方法对原始数据进行粗差识别与剔除,而后用MHSS ARAIM算法处理经粗差探测后的数据,可弥补MHSS ARAIM算法的不足。对若干IGS和全球连续监测评估系统iGMAS(international GNSS monitoring and assessment system)监测站观测数据进行计算和分析。结果表明:在航行LPV-200阶段,该算法应用于GPS和BDS导航的性能优于MHSS ARAIM;在假设单故障情况下,该算法对GPS和BDS观测数据的有效监视门限EMT(effective monitor threshold)的精度分别提高了22.47%和9.63%,对VPL(vertical protection level)的精度分别提高了32.28%和12.98%;在假设双故障情况下,对EMT的精度分别提高了80.85%和29.88%,对VPL的精度分别提高了49.66%和18.24%。     

COSMO-SkyMed颜色变换强度RC法南京建设用地变化检测

基于非局部滤波的SAR强度RC合成变化检测法对小图斑、线型地物等动态监测灵敏,且对数据获取无时空基线要求,在多云多雨城市地表要素变化检测中具备潜力。本文研究以多时相SAR强度RC合成图为数据源,提出一种基于色彩空间变换的变化图斑半自动提取方法,即通过色彩空间转换、训练样本选取、监督分类影像分割、变化区域提取4步骤,可实现基于SAR强度图的城市建设用地动态监测与图斑高效更新。选取南京河西新城与江北新区为示范,以最优参数配置（3特征向量与10样本类别）进行试验,实现了优于88%的建设用地查准率指标。     

新型移动三维激光扫描技术在盾构管片椭圆度检测中的应用

铁运营阶段对隧道结构的变形监测保证了地铁运行的安全,而椭圆度检测是地铁隧道结构检测的重要工作。本文简单介绍了传统椭圆度检测的基本方法,分析了新型移动三维激光扫描检测系统基本原理及隧道椭圆度检测的方法和处理流程。通过工程案例实际应用以及对检测结果的综合分析,证明了移动三维激光扫描技术在盾构管片椭圆度检测中的优势。     

"资源一号"02C卫星影像土地利用变化检测能力分析

“资源一号”02C卫星自成功发射以来,被广泛应用于土地资源、矿产资源、地质环境调查,以及国土资源、地质灾害应急监测等应用领域。“资源一号”02C卫星是一颗填补我国高分辨率遥感数据空白的卫星,是根据国土资源主体业务需求定制的第一颗国产高分辨率业务卫星。本文以吉林省和浙江省某试验区“资源一号”02C卫星数据为例,对“资源一号”02C卫星数据融合影像进行了土地利用遥感监测变化信息检测能力的方法试验。根据试验区数据变化信息提取试验结果,对“资源一号”02C卫星影像在土地利用动态遥感监测中的变化检测能力和适用性开展了试验、分析和评价工作。     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data

It is important to identify and locate glacial lakes for assessing any potential hazard. This study presents a combination of semi-automatic method Double-Window Flexible Pace Search (DFPS) and edge detection technique to identify glacial lakes using Sentinel 2A satellite data. Initially, Normalized Difference Water Index (NDWI) has been used to identify water and non-water areas, while DFPS and Edge detection technique has been used to identify an optimum threshold value to distinguish between water and shadow areas. The optimal threshold from DFPS process is 0.21, while threshold value of gradient magnitude using edge detection process is 0.318. The number of glacial lakes identified using the above algorithm is in close agreement with previously published results on glacial lakes in Gangotri glacier using different techniques. Thus, a combination of DFPS and edge detection process has successfully segregated glacial lakes from other features present in Gangotri glacier.     

An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

基于ETOPO1数据的全球及重点海域水深统计与分析

由于不同的水深探测手段及海深反演方法适用于不同深度的海域,且全球海域水深分布不均,因此在探测及反演海深之前,需对目标海域的水深分布有一定认识。本文采用ETOPO1模型对全球及部分海域进行统计分析,结果表明,全球及大西洋、印度洋、太平洋区域的水深大多处于3 000—10 000 m,其中3 000—5 000 m水深海域占比较大,因此,在这些海域主要采用重力数据结合船测数据进行反演,同时应注意避开海岸带、岛屿较多、海脊存在的区域;对于水深小于1 000 m的海域,可以根据外部环境,采用多波束船测、水深遥感、机载激光测深等方式进行探测。通过分析及统计全球和重点海域的水深可以为海深探测及海底地形反演方法的选取提供参考。     

深度卷积神经网络支持下的遥感影像飞机检测

针对YOLOv3算法对小目标检测较差及出现较多漏检的问题,本文提出了一种优化的YOLOv3算法.首先使用K-means算法计算出与数据集相适用的锚框;其次将扩张卷积引入到YOLOv3网络,用来增强网络高层的感受野,改善小目标的检测效果;然后使用深度可分离卷积取代YOLOv3网络残差模块中的普通卷积,可减少计算量,从而得到一种新型卷积神经网络结构;最后在数据集上进行对比试验.结果表明,优化的YOLOv3算法能够检测出更多目标,降低漏检率,相比于YOLOv3算法,其召回率提高11.86%,F1-score提高2.99%.