一种基于对等网络的云资源定位算法

提出用分布式哈希表(DHT)为每台云服务器产生一个唯一的节点编号,该编号作为网络拓扑结构、检索信息存储和信息查询共同的标志符,从而形成一个适合分布式计算的结构化P2P覆盖网.设计了新的拓扑和路由协议来解决云资源的常数跳定位问题.仿真实验表明,经典的P2P算法平均查找跳数与网络规模成正相关,无法依据云计算的实际需要人为地控制查找跳数;该算法的平均查找跳数与网络规模无关,随着网络规模的增大而趋向于设定值,可以解决云资源的常数跳定位问题.     

基于对等结点指针表优化的 Chord 算法改进

对等网络需要解决的一个关键性问题是如何有效地查找存储所需资源的结点.文中在研究分布式查找算法Chord 的基础上,介绍了分布式哈希表(DHT)的主要思想,阐述了资源关键字查找方式,重点分析结点指针表的特性及其表中冗余信息对查找资源的影响,进而提出了覆盖冗余信息的方法(URFChord)改进结点指针表. URFChord 方法首先要计算指针表的冗余量 R(N),然后在不增大指针表存储空间的情况下,删除指针表冗余信息再添加 R(N)个新的路由信息.通过性能分析及仿真实验,证实了这种改进方法的可行性和有效性,减少了平均查找路径长度,提高了查询效率     

数据包过滤规则的快速匹配算法和冲突检测

通过分析数据包过滤技术中的性能瓶颈,提出了过滤规则的快速匹配算法BSLT．该算法采用Trie数据结构存储规则表,并只在叶节点存储相应规则,节省了存储空间,其空间复杂度为O(NW),查找的时间复杂度为O(W);在匹配时采用二分法进行查找,提高了匹配速度,匹配的时间复杂度为O(N)．实验证明BSLT的吞吐率在100条规则内比顺序匹配算法提高了近20％,而且规则越多．BSLT的优势越明显．此外,分析了数据包过滤技术的另一个问题——规则冲突,给出了冲突的理论证明和查找算法．实验证明该算法能准确地检测出冲突规则．     

IGBP云检测网格产品升尺度方法及精度评价

国际地圈生物圈计划(international geo-biosphere program, IGBP)云检测网格产品是利用多特征阈值方法得到的云检测结果,云检测产品作为其他大气产品与陆地产品反演的辅助数据,也是目前云检测研究的检验依据。文章面向IGBP云检测网格产品的特点,提出了迭代聚类的升尺度方法。在此基础上,为在无真实参考数据条件下有效评价升尺度方法的效果,基于欧式距离(Euclidean distance, ED)设计了类内欧氏距离(inner class ED,ICE)和类间欧氏距离(ED between classes, EBC)两种精度评价方法。实验基于风云三号卫星数据,利用ICE、EBC,并结合占比法、相似度、互信息和均方根误差对不同升尺度方法结果开展了综合对比分析与讨论,验证了所提出方法的可行性与有效性。     

基于本体描述逻辑的云制造服务匹配方案

为了实现云制造环境下资源服务的增值和增效的目的,阐述了云制造的典型特征及云制造服务匹配的意义后,针对处理OWL-S语义描述云制造服务的问题,从服务请求及服务结果匹配两个核心方面入手,提出了一种基于本体描述逻辑(DL)的云制造服务匹配方案,并对该匹配方案中的各个阶段进行阐述,有效地提高了服务匹配的查全率和查准率。最终通过与传统关键字匹配方案和基于本体描述逻辑包含性检测的服务匹配方案进行比较分析,实验结果证明了文中提出的匹配方案具有很好的可行性和有效性。     

一种高效的网络安全设备IP数据包转发机制

通过对IP数据包转发机制研究,针对网络安全设备对数据包转发的特殊需求,提出了一种基于(IFPLUT TCAM)的IP数据包转发机制。该机制将查找表根据输出端口分割为若干个小查找表,并允许查找引擎对每个小查找表进行并行处理,有效地将寻找“最长前缀匹配”的复杂问题简化为“第一前缀匹配”问题。     

基于肤色的复杂环境下人脸检测

对由光源颜色变化引起的图像色彩偏差,进行了校正,并在YCbCr颜色空间建立了Cb-Cr色度查找表和亮度信息联合的肤色模型,应用预处理技术,去除部分非人脸区域,减少人脸检测的搜索空间,并采用模板匹配方法在人脸候选区域检测人脸.实验表明,该方法能够有效的从复杂环境的彩色图像中检测出左右旋转不超过45°的人脸,且不受人脸表情、尺度和数目的影响,且错误率较低.     

SIFT算法与折半查找法在产品表面缺陷检测中的应用

为了准确快速实现对产品结构质量的全方位完整检测,首先采用SIFT算法与折半查找法确定任意角度拍摄的待检产品在标准图像库中最优位置信息,其次根据先验知识将其转到待识别区域所在角度利用投影法分割出感兴趣区域,最后通过减影法及相关度计算判别有无缺陷;实验表明在保证检测准确率的前提下,文章所用匹配方法比传统全周向固定步长方法平均可节省2.08s。     

基于视觉语义与激光点云交融构建的SLAM算法

激光雷达作为同时定位与地图构建(SLAM)传感器之一,因精度高、性能稳定等特点而被广泛研究使用.但其获得的点云数据较稀疏,包含特征信息少,会导致误匹配、位姿估计误差大等问题,影响SLAM的定位和建图精度.对此,提出一种将视觉语义信息与激光点云数据融合的SLAM算法(VSIL-SLAM).首先,基于投影思想将聚类后的点云映射到语义检测框内,生成语义物体,解决原始激光点云特征稀缺问题;然后,在形状特征的基础上引入拓扑特征对语义物体进行表述,提出基于匹配的拓扑相似性度量方法,解决单一特征造成的误匹配问题,提高匹配准确度;最后,加入语义物体点到点的几何约束,基于几何特征和语义物体构建前端里程计,并完成后端回环检测和位姿图优化设计.实验结果表明,所提出算法在定位和建图效果上都有显著提高,改善了激光SLAM算法的性能.     

一种改进的RGB-D SLAM室内空间三维重建方法

提出一种改进的基于ORB(Oriented FAST and Rotated BRIEF)的RGB-D SLAM室内三维重建方法。前端利用改进的RANSAC(Random Sample Consensus)算法提升特征点匹配精度,结合PnP(Perspective-n-Point)实现点云图像的精确配准;后端采用位姿图进行优化,降低噪声数据对重建的影响;并利用回环检测控制重建过程中的误差累积。实验结果表明,所提出的特征点匹配方法能显著提高特征点的匹配精度,正确匹配率约为94%,较传统RANSAC算法提升6.5%;所提方法与传统RGB-D SLAM重建方法相比,重建结果质量更优,其中相机估计轨迹与真实轨迹互差RMS结果更佳,RMS值均小于0.08m。     

Night-time cloud detection for FY-3A/VIRR using multispectral thresholds

Night-time cloud detection using satellite data is a challenging area of research. This article presents a night-time cloud detection algorithm based on multispectral thresholds for the Visible and Infrared Radiometer (VIRR). VIRR is one of the keystone instruments on board the Chinese Feng Yun 3 (FY-3) polar-orbiting meteorological satellite. In this algorithm, three thermal infrared channels and other ancillary data are used to test for the presence of clouds according to different underlying surface types, and the four levels of possible cloud confidence are used to report whether a pixel is cloudy or clear. This algorithm strengthens the ability of identification of low cloud using the brightness temperature difference between the 3.7 and 12 μm channels. The comparisons of a new cloud mask with the official VIRR cloud mask product and with the official Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product are shown to illustrate and validate the effect of this new algorithm. In addition, this algorithm is applied to FY-3B/VIRR data to test the validity and accuracy of cloud detection.     

A daytime cloud detection algorithm for FY-3A/VIRR data

Cloud detection is essential for the retrieval of atmospheric and surface parameters and it directly impacts the quality of many satellite geophysical products used in weather, climate and environmental research. In this article, a daytime cloud detection algorithm based on multi-spectral thresholds is proposed to discriminate clouds from clear skies for the visible and infrared radiometer (VIRR), which is a key instrument on board the Chinese FengYun-3A (FY-3A) polar-orbiting meteorological satellite, launched 27 May 2008. The VIRR has ten bands in the wavelengths 0.43–12.5 μm and provides global observations of atmosphere, ocean and land in the visible and infrared regions of the spectrum. In this algorithm, the underlying surface is divided into five ecological types: snow/ice, desert, coastal, land and water, and seven spectral bands of the VIRR are used to indicate a level of confidence that the VIRR is observing clear skies. This algorithm also utilizes the 1.6 μm band and the difference between the 1.38 and 1.6 μm bands to respectively detect water cloud and high cloud. An example of cloud detection and a comparison with an official cloud masking product are given; the results show that this algorithm performs well and is better than the official algorithm in cloud detection.     

基于MODIS数据的白天多层云检测算法

分析了一种利用中分辨率成像光谱仪(MODIS)数据进行白天多层云检测算法。首先利用IMAPP软件包中的云检测算法对像元区域进行云检测,然后采用红外三通道(8.5μm、11μm和12μm)技术进行云相态判识,区分出单层水云和单层冰云,最后利用2.1μm反射率和11μm亮度温度双通道散点图,计算出多层云像元在2.1μm和11μm两个通道上的取值范围,从而识别多层云系。利用该算法对热带风暴云系进行了多层云检测试验,试验结果显示算法可简单有效地识别典型的多层云系。     

风云三号传感器红外通道海表比辐射率模拟

海表比辐射率（SSE）是从卫星被动遥感数据反演海表温度（SST）的关键参数之一,本文讲述了利用Wu andSmith（1997）发展的SSE模型,对搭载在风云三号（FY-3A）上的红外大气探空仪（IRAS）第8（12.47μm）、9（11.11μm）、19（3.98μm）和20（3.76μm）波段,中分辨率光谱成像仪（MERSI）第5波段（11.54μm）,可见光红外扫描辐射计（VIRR）第3（3.65μm）、4（11.00μm）和5（12.13μm）波段的SSE进行模拟计算。模拟结果表明,SSE是观测角度、波长和海表粗糙度（海面风速）的函数。模拟值与IRAS、MERSI和VIRR红外通道波谱响应函数进行卷积计算得到波段SSE。在观测角度小于60°时,波段SSE对海面风速不敏感,使用风速为8m/s的SSE替代其他风速条件下的SSE所引起的误差小于0.5%。另外,使用波段SSE计算卫星观测值所导致的误差不超过0.05K,可以忽略不计。波段SSE随观测角度变化的规律可以用高斯函数进行描述,拟合误差小于0.02%,相关系数为1.000。本文的模拟结果可以用于SST的精确反演。     

缺少水陆标识数据情况下MODIS图像云检测的实现

云检测中必须进行像元的水陆判识,云的存在给水陆判识带来了困难.因此,云检测中总是利用水陆标识数据辅助云检测.本文讨论了缺少水陆标识数据的云检测方法,并证明了该方法可行、实用.     

Inter-calibration of VIRR/FY-3A/B split-window channels with AIRS/Aqua and IASI/Metop-A measurements

This work addressed the inter-calibration of the two split-window channels 4 and 5 (IR4, around 10.8 µm; IR5, around 12.0 µm) of Visible and Infrared Radiometer (VIRR) aboard FengYun 3 (FY-3) A and B satellites against the hyperspectral channels of Atmospheric Infrared Sounder (AIRS) on Aqua and Infrared Atmospheric Sounding Interferometer (IASI) on Metop-A over the Arctic area (180ºW–180ºE, 60ºN–90ºN) using the hyperspectral convolution (HSC) method in June and December of 2010–2013. First, the satellite data were re-sampled into the gridded study area using the area-weighted pixel-aggregation method. Then, besides the collocation condition, the measurements acquired by different instruments were matched with the following criteria: 1) the absolute observation time difference is less than 20 min (|?time|< 20?), and 2) |cosθ₁ /cosθ₂ – 1|< 0.015, where θ₁ and θ₂ are the at-surface view zenith angles (VZAs) of the VIRR/FY-3 measurement and the hyperspectral measurement, respectively. Finally, linear regression analyses on the matching measurements were carried out to obtain the inter-calibration coefficients, and re-calibrated VIRR/FY-3A/B measurements were achieved. The results showed that the calibrations of AIRS/Aqua and IASI/Metop-A channels were quiet consistent, whereas, against the hyperspectral channels, the calibrations of VIRR/FY-3A/B channels 4 and 5 were biased, and they presented certain seasonal and annual variations, especially for VIRR/FY-3A channel 4. In contrast to VIRR/FY-3A channels, the calibration discrepancies of VIRR/FY-3B channels 4 and 5 were smaller. Moreover, no obvious impact of solar illumination on VIRR/FY-3A/B measurements was observed.     

基于知识库的面向对象卫星云图分类

为了提高卫星云图分类精度和实时识别云类,基于云类知识库采用面向对象的分类方法对卫星云图进行分类。首先对2011年7~8月的FY\|3A/VIRR卫星云图进行预处理,从中裁截500个云样本,随机选取42%云样本作为训练样本,提取训练样本的光谱和纹理特征,基于ReliefF方法进行特征选择,采用反向传播神经网络进行训练构造分类器,利用剩余58%云样本进行网络测试,至此云类知识库构建完毕。然后对待解译的云图进行JSEG分割获取云对象,基于云类知识库已训练好的分类器实现面向对象的云图分类。试验结果表明:所设计的云图分类算法有效,分类结果与云分类产品数据基本达到一致。     

Object-based cloud and cloud shadow detection in Landsat imagery

A new method called Fmask (Function of mask) for cloud and cloud shadow detection in Landsat imagery is provided. Landsat Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) are used as inputs. Fmask first uses rules based on cloud physical properties to separate Potential Cloud Pixels (PCPs) and clear-sky pixels. Next, a normalized temperature probability, spectral variability probability, and brightness probability are combined to produce a probability mask for clouds over land and water separately. Then, the PCPs and the cloud probability mask are used together to derive the potential cloud layer. The darkening effect of the cloud shadows in the Near Infrared (NIR) Band is used to generate a potential shadow layer by applying the flood-fill transformation. Subsequently, 3D cloud objects are determined via segmentation of the potential cloud layer and assumption of a constant temperature lapse rate within each cloud object. The view angle of the satellite sensor and the illuminating angle are used to predict possible cloud shadow locations and select the one that has the maximum similarity with the potential cloud shadow mask. If the scene has snow, a snow mask is also produced. For a globally distributed set of reference data, the average Fmask overall cloud accuracy is as high as 96.4%. The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images.     

Derivation and Comparison of Water Vapor between Infrared Channels and Near Infrared Channels from FY\|3

Atmospheric water vapor content has important significance for radiometric correction of satellite image,understanding of atmospheric micro\|physical process,precipitation prediction and so on.We will retrievie atmospheric column water vapor based on Moderate Resolution Imaging Spectroradiometer (MERSI)near infrared channels and Visible and Infrared Radiometer (VIRR)thermal infrared channels datas of FY\|3A,respectively.Comparing the retrieval results of MERSI,VIRR and the observations of sounding ground stations respectively,we find :① the observed correlation between retrieval results of MERSI and observations is 0.763,while retrieval results of VIRR has poor correlations with observations,which is0.169.What’s more,The retrieval accuracy of MERSI (1.108 g/cm2)is higher than that of VIRR (1.894 g/cm2);②The average atmospheric column water vapor with three channels of MERSI has higher retrieval accuracy than channel 17th(1.133 g/cm2),18th(1.424 g/cm2),19th(1.827 g/cm2).The main reason is that three channels have different sensitivities of water vapor,and utilizing three channels to retrieve atmospheric column water vapor content can reach the effect of perfection.     

基于FY-3A/VIRR数据的雪盖信息提取软件设计与实现

目前基于FY-3A/VIRR资料的处理研究较少且VIRR数据量庞大,一些商用遥感图像处理软件很难直接完成对图像的预处理工作,这样对后续的定量化反演以及对FY-3A/VIRR的推广使用带来了困难。为了解决业务化问题,运用改进的归一化积雪指数(NDSI)、综合阈值判别算法和IDL、VB混合编程技术相结合的方法设计了积雪信息批量提取软件,实现了针对FY-3A/VIRR数据的单幅图像或多幅图像的积雪信息提取以及精度验证。实验表明,该软件处理速度快、实时性好、可批量提取积雪信息,大大节省了人力资源,同时提高了VIRR数据的分发和共享能力,可以在今后的工业生产和自动化领域推广使用。