A New Feature Vector Using Selected Line Spectra for Pulsar Signal Bispectrum Characteristic Analysis and Recognition

Average pulse profiles of pulsar signals are analyzed using the bispectrum tech-nique. The result shows that there are nonlinear phase couplings between the two frequency axes of the bispectrum charts, which indicate nonlinear factors in the generation and prop-agation of pulsar signals. Bispectra can be used as feature vectors of pulsar signals because of their being translation invariant. A one-dimension selected line spectrum algorithm for ex-tracting pulsar signal characteristic is proposed. Compared with selected bispectra, the pro-posed selected line spectra have the maximum interclass separability measurements from the point of view of the whole one-dimension feature vector. Recognition experiments on several pulsar signals received at several frequency bands are carried out. The result shows that the selected line spectrum algorithm is suitable for extracting pulsar signal characteristics and has a good classification performance.     

Airborne near-real-time monitoring of assembly and parking areas in case of large-scale public events and natural disasters

A critical requirement for an effective and coordinated response by public entities tasked with management, security, and relief during large-scale public events or natural disasters is the availability of current situational information. However, today there is a lack of comprehensive operational systems allowing a near-real-time (NRT) collection, visualization, and provision of situational information for larger areas. In this study a methodological framework is proposed, which allows an NRT extraction and visualization of situational information based on aerial image acquisition. The framework combines digital image analysis using a generic supervised information extraction approach based on statistical modeling with a downstream web-based visualization component realized through an automatic update of web services. Even though being applicable for different scenarios, the workflow will be demonstrated for the specific use-case of a NRT monitoring of open spaces including assembly and parking areas. Compared to other approaches, image analysis results indicate a high robustness and a low demand for computational power sources (7 seconds per image). Due to a high degree of automation, the proposed workflow contributes to a NRT ‘end-to-end’ monitoring system, which was developed within the VABENE (German acronym for ‘traffic management under large-scale public events and disaster conditions’) project covering all parts from the acquisition of raw aerial imagery to the dissemination of information products to end-users.     

A new insight into land use classification based on aggregated mobile phone data

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.     

基于遗传小波神经网络的海底声学底质识别分类

分割海底声纳探测图像,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,构建小波神经网络。利用遗传算法优化小波神经网络的初始权值及小波参数,对砂、礁石、泥3种底质类型分别进行训练,并得到3种底质的测试精度都在90%以上,优于单独利用小波神经网络进行训练时的测试精度,克服了小波神经网络训练时易陷入局部极小的固有缺陷,表明基于遗传算法的小波神经网络可有效用于海底底质声纳图像的识别和分类。     

对《土地利用现状分类》中有关问题的探讨

按照土地的属性、利用方式和覆盖特征,结合土地利用特点,对土地利用现状分类含义进行了深入的研究,认为部分土地分类的含义值得商榷,并提出了解决问题的途径.     

AdaTree算法在遥感影像分类中的应用

目前的遥感影像分类研究中,决策树的生成完全依赖于现有的数据挖掘软件,缺少对决策树算法的深入研究和改进。本文以遥感影像分类为背景,采用BoostTree算法作为模型,通过算法改进构建了一种新的复合决策树算法———AdaTree,并以该算法为基础,设计实现了决策树遥感影像分类系统。以AdaTree算法作为分类器,分别对Landsat7ETM+影像和WordView2影像进行了基于像元和面向对象的分类实验,并与BoostTree和SVM算法进行了比较。实验结果表明,AdaTree算法在分类精度上要优于BoostTree和SVM算法,平均Kappa系数分别达到0.905 2和0.939 8。     

AdaTree算法在遥感影像分类中的应用

为了进一步提高遥感图像分类的精度,提出了一种基于Log-Gabor小波和Krawtchouk矩的遥感图像分类算法。首先利用Log-Gabor小波对遥感图像进行多方向、多分辨率滤波,提取遥感图像的纹理特征;同时计算遥感图像的Krawtchouk矩不变量,作为遥感图像的边缘形状特征,并与基于Log-Gabor小波提取的纹理特征构成完整的特征向量;最后依据所提取的特征向量利用支持向量机(support vector machine,SVM)分类器对待分类图像进行分类,得到最终的遥感图像分类结果。实验结果表明,与近年来提出的基于Gabor小波、基于Log-Gabor小波、基于Krawtchouk矩等3种遥感图像分类算法相比,本文算法在主观视觉效果和分类精度等客观定量评价指标上都有了明显的改善,是一种行之有效的遥感图像分类算法。     

Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game

Volunteered geographic information (VGI) is the assembly of spatial information based on public input. While VGI has proliferated in recent years, assessing the quality of volunteer-contributed data has proven challenging, leading some to question the efficiency of such programs. In this paper, we compare several quality metrics for individual volunteers’ contributions. The data were the product of the ‘Cropland Capture’ game, in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of 6 months. We compared agreement between volunteer ratings and an image's majority classification with volunteer self-agreement on repeated images and expert evaluations. We also examined the impact of experience and learning on performance. Volunteer self-agreement was nearly always higher than agreement with majority classifications, and much greater than agreement with expert validations although these metrics were all positively correlated. Volunteer quality showed a broad trend toward improvement with experience, but the highest accuracies were achieved by a handful of moderately active contributors, not the most active volunteers. Our results emphasize the importance of a universal set of expert-validated tasks as a gold standard for evaluating VGI quality.     

数据挖掘在高校学生管理决策中的应用模式分析

随着因特网的广泛应用,各高校在开展现代远程教育过程中记录了大量复杂的学生信息,基于这些历程信息,在高校学生管理决策中利用数据挖掘技术,提出了现代高校学生管理工作的新模式,并就几个管理决策的应用模式进行了实例分析.     

城市地下空间安全监测与预警指标研究

为保障城市地下空间开发利用的安全性,促进城市可持续发展,通过文献调研、现场调查和专家咨询等方法,分析提出城市地下空间监测的五项原则,将监测对象划分为三类：工程结构本体、周围岩土体以及周边环境。将监测指标归纳为变形类、力学类、振动类和宏观状态类共四类,其中变形类指标执行双控要求,其他三种指标执行单控要求。监测趋势预测分析可采用公式法、回归分析法、时间序列分析法、灰色预测法、神经网络法和支持向量机法等。全国各地监测控制值基本一致,但预警分级标准存在地区差异,其中北京市和广州市分级预警具有较大参考价值。目前城市地下空间安全监测存在七项不足：预警分级标准不完善,人工监测效率低,监测参数单一,监测信息缺少共享协同,测量精度较低,重监测轻预测以及缺乏数据融合和机器学习应用。针对这些问题,可采取七项措施进行改进：建立合理预警分级标准,发展自动化与智能化监测,多参数综合监测,应用远程监测与云平台,开发高精度测量设备,监测和预测并重,以及数据融合与机器学习应用。