HJ-1A/B卫星CCD影像大气订正

大气订正是遥感信息定量化研究中必不可少的一步,目前已有一些成熟的方法,但由于HJ-1A/B卫星CCD相机波段设置特点,常规的大气订正方法基本不适合于HJ-1A/BCCD影像.本文在大量分析HJ-1A/BCCD影像中不同地物的多种指数基础上,提出了改进暗目标法实现HJ-1A/BCCD影像的大气订正,该方法采用比值植被指数(RVI)、土壤调整植被指数(SAVI)和归一化水体指数(NDWI)的综合分析法确定暗像元自动提取,使之适用于环境减灾卫星CCD影像数据.为了客观地验证该方法的精度,本文选取地表平坦均一的敦煌校正场作为实验区,通过多次测量卫星过境时的地表反射率进行分析验证.     

基于CBERS-02卫星数据和地面测量的生物量估算及其影响因素分析

以中国科学院千烟洲生态站及其周围地区为研究区域, 利用CBERS-02卫星的CCD数据和地面野外样方调查数据, 探讨了千烟洲主要人工林马尾松、湿地松的植被指数NDVI和生物量之间的关系模型. 和千烟洲地面生物量调查数据结果对比, 表明利用CBERS-02卫星CCD的NDVI可以用于生物量估算, 但NDVI和生物量模型具有一定的局限性, 即这种模型依赖不同的树种. 利用CBERS-02卫星高分辨率数据进行的不同空间尺度分辨率下NDVI对生物量估算的影响因素分析表明, 对于植被覆盖比较大的千烟洲地区, NDVI的非线性特征对NDVI尺度扩展影响很小; 但随着像元空间尺度的扩展, 像元NDVI值发生了相应的变化; 而由于空间尺度扩展引起的像元类型(属性)的变化, 会由于生物量模型的适用性差异而使生物量估算产生比较的大误差.     

近红外比值法在对流层航空遥感数据大气水汽估算中的改进

利用近红外波段大气窗口通道和水汽吸收通道辐亮度比值反演大气柱水汽含量,是卫星遥感大气水汽估算的通用方法之一.但对于对流层内的航空遥感水汽估算,直接套用卫星遥感水汽估算近红外比值法会引入飞行平台到大气顶层水汽的影响.根据航空遥感成像特征,利用Modtran和热力学初始分析资料(thermodynamic initial guess retrieval,TIGR)大气廓线库数据,分别构建入射路径上,航飞高度到地表的水汽透过率与太阳到地表水汽透过率的对数之比G与航飞高度内大气水汽与整层大气水汽之比R,以及入射路径上的航飞高度到地表的水汽透过率,与出射路径上地表到入瞳处水汽透过率的对数之比H与太阳入射角qs的函数关系,结合下垫面特征,建立对流层航空遥感水汽估算模型.以1614组TIGR廓线为输入模拟航飞入瞳处辐亮度,利用本文模型估算对流层内大气水汽,并与廓线数据直接计算值对比,结果表明,当航飞高度在1.0~7.0 km时,模型估算值的总体精度为0.22 g/cm~2,且精度优于0.5 g/cm~2的样本占总样本数95.30%.利用2014年5月28日郑州上街航空遥感试验获取的影像进行水汽分布估算,并与同步大气探空数据计算到的水汽进行对比,结果表明,各样区估算值与探空值的RMS误差为0.16 g/cm~2(12.8%),且对下垫面覆盖条件的先验了解能够提高模型估算精度.本文模型消除航空遥感飞行高度以上大气的影响,增大了模型的精准度与适应性,为热红外航空遥感数据实时大气校正提供了可靠的输入.     

高光谱卫星海洋遥感资料辐射精度评价模型研究

对于海洋水色遥感卫星来说,辐射定标精度作为一项重要的核心指标,恰恰是目前我国自主海洋水色卫星存在的不足之处,需要对遥感器进行系统的在轨辐射测量精度评价.文中基于大气辐射传输理论开发了一套高光谱卫星海洋遥感资料辐射精度评价模型(HRSREM),可以比较精确地计算出卫星平台接收到的遥感反射率,来衡量可见光遥感数据的测量精度.通过Gordon查找表方法对HRSREM模型的大气Rayleigh散射和气溶胶散射分量的精度验证,表明模型对两种大气散射计算误差小于2%;利用海上现场ASD高光谱仪测量的天空光反射率对模型的前向散射计算结果验证,其光谱平均相对误差约5.4%;利用宽视场海洋水色扫描仪(SeaWiFS)数据对模型的大气顶遥感反射率计算精度进行验证,其相对误差小于3.5%,说明HRSREM模型对卫星遥感资料的光谱辐射测量评价具有很高的精度.利用HRSREM模型对高光谱遥感卫星Hyperion在澳大利亚附近海域测量数据进行精度估算,可见近红外波段的光谱平均相对误差约为7.3%;对中分辨率成像光谱仪(CMODIS)数据精度评价结果表明,CMODIS存在较大的测量误差,特别是近红外波段的定标系数明显偏大,需要重定标来提高定量化处理和应用水平.     

CBERS-02B数据在霍山山前活动断裂带1:5万填图中的应用

利用中巴地球资源卫星02B数据,开展山西霍山山前活动断裂带遥感解译研究,解译获得该断裂带总延伸长度约116km,整体走向NE,按其几何结构可以分为6段,断裂活动在晚更新世以来地层形成的断层陡坎在影像上特征清晰;结合野外调查,认为霍山山前断裂带在全新世以来以垂直运动为主.高分辨率遥感卫星数据可以获取活动构造定量研究中断裂带的长度和分段长度,通过断层构造地貌的解译,可以识别出包括断层陡坎、断层槽谷等构造地貌变形信息,为筛选野外重点工作区域提供帮助.研究表明,高分辨率的中巴地球资源卫星可以有效应用于1:5万活动断层填图工作及活动构造定量研究中.     

用CBERS-02卫星和MODIS数据联合反演地表蒸散通量

用低分辨率遥感数据计算地表蒸散通量时, 如何减小空间尺度误差是需要考虑的重要问题. 在用MODIS数据计算蒸散时尝试加入高分辨率中巴资源卫星CBERS-02提供的地表分类图像, 根据此分类信息在每个MODIS 1 km像元内统计各种地表类型所占的面积比例, 并分别计算各亚像元的热量通量, 然后用面积权重平均的方法得到MODIS像元的热量通量. 结果表明, 将CBERS高分辨率地表分类数据和MODIS低分辨率遥感数据结合, MODIS 1 km混合像元的土壤热通量、显热通量和潜热通量都有所改变. 进一步分析这种变化产生的原因, 结合地面实验观测数据的验证, 作者推断, 经过处理以后, 混合像元的显热通量和潜热通量的误差都有所减小.     

嫦娥二号卫星非对称降轨控制技术

嫦娥二号任务中,为完成对预选着陆区高分辨率成像,需在月球背面实施轨道机动以满足成像区域的高度要求.采用目标近月点对面或燃料最优的降轨控制方式,轨控关机将在月球背面地面不可见的情况下完成.为了确保卫星安全,工程要求关机点控制在国内测站跟踪弧段内,并满足卫星应急处置要求.根据任务需要,飞控中心采用了非对称降轨控制方法,对此次轨控的控制点进行偏置,采用迭代方法求解有限推力方式下的轨控参数,并在迭代过程中对目标近月点漂移进行修正.2010年10月26日,嫦娥二号卫星成功降轨,在100km×15km的试验轨道段上飞行32圈,完成了对预选着陆区高分辨率成像.本文在分析降轨控制约束条件和两种常规控制方式的基础上,阐述了非对称降轨控制技术的原理和实现方法,并通过实施数据对轨控进行了效果评价.     

基于分段辐射校正的星载TDI-CCD成像数据辐射处理方法

文中在分析现有的相对辐射校正方法的局限性基础上,提出了一种基于分段辐射校正的星载TDI-CCD成像数据辐射处理方法.该方法利用实验室定标数据对探元不同感光范围的光电响应模式采用不同的模型进行拟合,从而提高定标系数或者查找表的拟合精度.文中选取资源1号02BHR光学影像进行实验,实验结果表明文中提出的方法与现有的方法相比有较大提高.     

基于嫦娥一号卫星激光测高数据的月球DEM及高程分布特征模型

嫦娥一号卫星激光高度计获取了820多万个有效记录点,为了探索利用嫦娥一号卫星激光测高数据生成高精度的月球数字高程模型,本文提出了层次多结点样条算法,该算法利用一系列从粗糙到精细的多结点样条控制网格来逐步逼近或插值给定的激光测高数据.基于该算法,利用嫦娥一号卫星激光测高数据,生成了空间分辨率为0.0625°×0.0625°的全月球数字高程模型,并将该模型分别与ULCN2005和CLTM-s01及日本"月女神"模型进行了对比.同时,探讨了月面高程分布规律,并建立了嫦娥一号卫星高程的分布特征模型.全月球、月球正面高程呈正偏态、高峰态的正态分布;月球背面高程呈正偏态、低峰态的正态分布.     

赣江流域高分辨率卫星降水数据的精度特征与时空变化规律

提出了一种基于地面雨量计空间插值数据,从栅格单元、子流域以及全流域3种空间尺度上综合验证和比较卫星降水数据精度的方法.采用该方法,针对长江中下游赣江流域,以2003~2009年325个雨量站空间插值数据为基准,在日时间尺度上,综合评价了TRMM 3B42,TRMM 3B42RT,CMORPH,PERSIANN,GSMaP MWR＋和GSMaP MVK＋共6种高分辨率卫星降水数据的精度,揭示了精度指标的时空变化规律.结果表明经地面雨量计校正的3B42综合精度最高,在日均意义上略为高估了基准降水量,其他未经校正的5种数据均有较大程度低估.同时,6种数据在汛期的精度显著高于非汛期.随着时间尺度的扩展,卫星降水数据的精度有不同程度提高,其中3B42最明显.在赣江流域西部山区及北部山前区,卫星降水数据的精度较低,而在中部及东南部丘陵、盆地相对较高.随着空间尺度的扩展,6种数据的精度不断提高.本文为验证其他区域卫星降水数据精度提供了范例,并为进一步研究利用卫星降水数据分析流域水文过程提供了直接依据.     

Analysis of the relationship between LSTM network traffic flow prediction performance and statistical characteristics of standard and nonstandard data

The effectiveness of road traffic control systems can be increased with the help of a model that can accurately predict short-term traffic flow. Therefore, the performance of the preferred approach to develop a prediction model should be evaluated with data sets with different statistical characteristics. Thus a correlation can be established between the statistical properties of the data set and the model performance. The determination of this relationship will assist experts in choosing the appropriate approach to develop a high-performance short-term traffic flow forecasting model. The main purpose of this study is to reveal the relationship between the long short-term memory network (LSTM) approach's short-term traffic flow prediction performance and the statistical properties of the data set used to develop the LSTM model. In order to reveal these relationships, two different traffic prediction models with LSTM and nonlinear autoregressive (NAR) approaches were created using different data sets, and statistical analyses were performed. In addition, these analyses were repeated for nonstandardized traffic data indicating unusual fluctuations in traffic flow. As a result of the analyses, LSTM and NAR model performances were found to be highly correlated with the kurtosis and skewness changes of the data sets used to train and test these models. On the other hand, it was found that the difference of mean and skewness values of training and test sets had a significant effect on model performance in the prediction of nonstandard traffic flow samples.     

手舟骨的有限元建模及其损伤分析

目的 基于医学DICOM格式的图像,不经过二次转换并快速且准确的建立舟骨有限元模型,从而得到可以用于舟骨应力分析的有限元模型,并通过模拟手舟骨损伤来分析不同的受力特点. 方法 选择健康成年男性志愿者,进行高精度螺旋CT扫描,将得到的DICOM 3.0医学图像数据,导八MIMICS医学影像软件并生成实体模型,然后应用通用有限元分析软件ANSYS 11生成有限元模型.约束边界条件,模拟舟骨轴向压力进行加载,得出舟骨有限元模型上的应力分布与应变结果,并模拟舟骨不同损伤的力学改变. 结果 建立的舟骨三维有限元模型共有节点73588个,单元397344个.所建模型结构完整,空间结构准确,单位划分精细,轴向压力作用下加栽模拟形象、逼真,客观反映舟骨三维解剖形态结构和生物力学特点. 结论 应用高精度螺旋CT成像的DICOM 3.0数据,并与MIMICS和ANSYS相结合的方法快速建立舟骨的三维有限元模型是切实有效和可行的;有限元方法可较好的分析手舟骨及其损伤的生物力学特点.     

低维特征空间中基于旋转图像的三维环境模型配准方法

近年来,异类机器人之间(如飞行机器人和地面机器人)的协作成为机器人学研究发展的一个新的领域.异类机器人协作的难点之一是协作环境建模,而由于所获得的环境模型具有不同的观测视角和尺度,其环境建模中的模型配准是一个难点和关键.目前,能够适用于大视角差、大尺度差场景配准的方法并不多,基于旋转图像的配准方法被认为是一种可行方案,但其中存在的计算负担大和在野外环境中的鲁棒性差使得其也很难在实际系统中应用.基于此,面向三维点云环境模型,以旋转图像为基础,提出了一种新的基于低维特征空间的模型配准方法.首先,通过引入模型曲率、旋转图像熵值和激光反射强度3个特征构建了一个三维特征空间,得到候选对应点集合.然后,在候选对应点集合中利用旋转图像的方法查找正确的对应关系,实现模型配准.由于低维特征空间的引入,基于旋转图像特征的对应点搜索区域大大减小,因此算法计算效率得到了极大改善.同时由于引入的新特征与场景旋转图像特征的互补性,算法的鲁棒性和精确性也得到了提升.这些性能改进最后通过实验得到了验证.     

A Flexible Functional Form Approach To Mortality Modeling: Do We Need Additional Cohort Dummies?

The increasing amount of attention paid to longevity risk and funding for old age has created the need for precise mortality models and accurate future mortality forecasts. Orthogonal polynomials have been widely used in technical fields and there have also been applications in mortality modeling. In this paper we adopt a flexible functional form approach using two‐dimensional Legendre orthogonal polynomials to fit and forecast mortality rates. Unlike some of the existing mortality models in the literature, the model we propose does not impose any restrictions on the age, time or cohort structure of the data and thus allows for different model designs for different countries' mortality experience. We conduct an empirical study using male mortality data from a range of developed countries and explore the possibility of using age–time effects to capture cohort effects in the underlying mortality data. It is found that, for some countries, cohort dummies still need to be incorporated into the model. Moreover, when comparing the proposed model with well‐known mortality models in the literature, we find that our model provides comparable fitting but with a much smaller number of parameters. Based on 5‐year‐ahead mortality forecasts, it can be concluded that the proposed model improves the overall accuracy of the future mortality projection. Copyright © 2016 John Wiley & Sons, Ltd.     

Selection of Value‐at‐Risk models

Value‐at‐Risk (VaR) is widely used as a tool for measuring the market risk of asset portfolios. However, alternative VaR implementations are known to yield fairly different VaR forecasts. Hence, every use of VaR requires choosing among alternative forecasting models. This paper undertakes two case studies in model selection, for the S&P 500 index and India's NSE‐50 index, at the 95% and 99% levels. We employ a two‐stage model selection procedure. In the first stage we test a class of models for statistical accuracy. If multiple models survive rejection with the tests, we perform a second stage filtering of the surviving models using subjective loss functions. This two‐stage model selection procedure does prove to be useful in choosing a VaR model, while only incompletely addressing the problem. These case studies give us some evidence about the strengths and limitations of present knowledge on estimation and testing for VaR. Copyright © 2003 John Wiley & Sons, Ltd.     

Single‐season heteroscedasticity in time series

We consider seasonal time series in which one season has variance that is different from all the others. This behaviour is evident in indices of production where variability is highest for the month with the lowest level of production. We show that when one season has different variability from others there are constraints on the seasonal models that can be used; neither dummy and trigonometric models are effective in modelling this type of behaviour. We define a general model that provides an appropriate representation of single‐season heteroscedasticity and suggest a likelihood ratio test for the presence of periodic variance in one season. Copyright © 2007 John Wiley & Sons, Ltd.     

Forecasting Time Series with Trading Day Variation

Some levels of economic activity change over the days of the week. Also, the composition of the calendar changes over the years so that a particular month contains a different configuration of days of the week each year. The effects of the changing composition of the calendar upon a monthly time series is called trading day variation. This paper discusses one way to model trading day variation in monthly time series and how this model can be used to obtain improved forecasts over univariate ARIMA models. The ideas are illustrated on an actual data set.     

基于压缩感知思想的图像分块压缩与重构方法

压缩感知理论在数据获取、数据存储/传输、数据分析和处理方面有很大优势,成为近年来的研究热点.考虑到大多数图像信号信息分布有差异,编码端,在对图像分块的基础上,融合熵估计和边缘检测方法计算各图像块的信息含量,再从两个不同的角度进行分类采样:依据信息量多少将图像块分为平滑、过渡和纹理3类,使用不同的采样率采样;依据信息量的分布特征,采用不同的采样率分配策略进行采样.在解码端,根据不同类型的图像块构造不同的线性算子进行重构,再运用改进的迭代阈值算法去除块效应和噪声.实验证明,算法在提升图像重构质量的同时缩短了重构时间,并且对纹理边缘多的图像的重构效果较其他方法理想.     

A Comparison of Box—Jenkins and objective methods for determining the order of a non-seasonal ARMA Model

This paper evaluates different procedures for selecting the order of a non-seasonal ARMA model. Specifically, it compares the forecasting accuracy of models developed by the personalized Box-Jenkins (BJ) methodology with models chosen by numerous automatic procedures. The study uses real series modelled by experts (textbook authors) in the BJ approach. Our results show that many objective selection criteria provide structures equal or superior to the time-consuming BJ method. For the sets of data used in this study, we also examine the influence of parsimony in time-series forecasting. Defining what models are too large or too small is sensitive to the forecast horizon. Automatic techniques that select the best models for forecasting are similar in size to BJ models although they often disagree on model order.     

Forecasting Monetary Policy Decisions in Australia: A Forecast Combinations Approach

This paper applies a triple‐choice ordered probit model, corrected for nonstationarity to forecast monetary decisions of the Reserve Bank of Australia. The forecast models incorporate a mix of monthly and quarterly macroeconomic time series. Forecast combination is used as an alternative to one multivariate model to improve accuracy of out‐of‐sample forecasts. This accuracy is evaluated with scoring functions, which are also used to construct adaptive weights for combining probability forecasts. This paper finds that combined forecasts outperform multivariable models. These results are robust to different sample sizes and estimation windows. Copyright © 2011 John Wiley & Sons, Ltd.