出租车数据的时间序列谱聚类分析

城市空间运行的出租车产生大量的OD数据,数据的时序呈现周期性特点,客观反映人们的出行行为模式,本文采用谱聚类算法对北京五环区域内各空间单元的出行特征及其相似性进行探究。由于空间单元的时空行为特征受空间邻域和功能区划的影响,研究添加邻域因子和功能区因子以改进时间序列的相似性度量方法,从而实现时间序列谱聚类算法的空间和功能区拓展,进而增加人们对不同时空条件下出行行为特征的了解,以便对不同空间单元提供差异性服务,如不同时段公交的发车频次、动态调整商场营业时间、不同时空环境出租车候车点的实时变换、调控和优化不同区域服务保障等,将有助于降低城市能耗,更加合理地利用资源,也有助于居民实现智慧生活。     

基于Stacked ConvLSTM的时间序列森林火烧迹地检测

确定森林火烧迹地的准确时间点以及空间范围对于森林的受损评价、管理、碳核算以及森林恢复的管理有重要意义。由于森林火烧迹地在空间分布上具有一定的连续性,现有的森林火烧迹地提取方法大都采用先分类再后处理的两步处理策略来抑制虚警像素的影响。本文提出将时空检测方法 Stacked ConvLSTM用于时间序列森林火烧迹地的检测,在保持结果具有较好空间连续性的基础上避免了具有主观性的后处理操作,实现端到端提取森林火烧迹地信息,提升了森林火烧迹地的提取精度。采用MODIS时间序列数据,基于2001年—2008年以及2001年—2016年的黑龙江沾河林业局伊南河林场和内蒙古自治区毕拉河林业局北大河林场两个区域的历史时间序列,分别对这两个区域2009年以及2017年发生的特大火灾区域进行火烧迹地检测,利用Stacked ConvLSTM、Stacked LSTM以及bfast算法在两个区域的MODIS时间序列中提取森林火烧迹地,并将火烧迹地检测结果与ESA发布的Fire＿CCI 5.1火烧迹地产品进行对比分析。结果表明：首先,从目视效果来看,在研究区域Ⅰ,Stacked ConvLSTM检测的结果比St...     

遥感时间序列影像变化检测研究进展

同一区域、不同时期大量历史数据的积累,以及同一区域能够方便地获取高时间分辨率遥感数据,使遥感时间序列影像变化检测成为近年来遥感技术与应用的研究热点。本文系统总结和评述了当前遥感时间序列影像变化检测的相关研究进展和应用状况,在阐明遥感时间序列分析的意义,以及时间序列影像在变化检测中的优势的基础上,从非遥感领域时间序列变化检测方法出发,针对遥感时间序列影像变化检测的需求,明确和归纳了遥感时间序列变化检测的问题与类型,并对当前最新研究进行了综述,总结了各种方法的优点与不足,重点介绍了基于经验模态分解的遥感时间序列影像异常信息检测方法和基于隐马尔可夫模型的土地利用/覆盖变化检测方法,以期能够为相关研究提供参考。最后总结了该研究领域的发展趋势和存在问题,并对今后的研究工作和未来发展方向进行了展望。     

Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data

Optimal paths computed by conventional path-planning algorithms are usually not “optimal” since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.     

归一化植被指数在遗址探测中的应用

针对目前遗址探测研究多基于单时相遥感数据开展,存在偶然性,对最佳探测时间的研究较少等问题,该文以洛阳盆地为研究对象,利用多时相遥感数据,通过时间序列谐波分析算法(HANTS)重构时间序列植被指数数据集,去噪的同时对比分析出利用冬小麦长势信息进行地下遗址遥感监测的最佳时间区间。研究表明,受地下遗址的胁迫,在分蘖期,冬小麦长势明显比非遗址区的长势差,表明该时期是进行地下遗址探测的最佳时期,进而对最佳探测时期内NDVI积分,有效增强了遗址区和非遗址区之间差异,突出地下遗址的位置和轮廓信息。利用该研究成果,文章成功探测到汉魏洛阳故城以及古伊洛河异常区,与现有的考古资料相吻合。     

Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach

The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.     

基于时间序列统计特性的森林变化监测

森林动态变化分析对揭示生态系统环境变化及植被恢复和布局重建等具有重要意义,时间序列的遥感数据为森林监测提供了基础数据。本文根据森林植被的统计学特性,在暗目标法的基础上,利用归一化植被指数NDVI实现森林样本自动选择;并融合NDVI构建了新的综合森林特征指数(Integrated Forest Z-Score,IFZ);以时间序列的IFZ分析森林动态信息,实现森林变化动态监测。以三峡大坝及周边区域森林为研究区,利用2001年至2012年每年生长季节(5月—10月)的Landsat TM影像检验本文算法。基于2002年、2006年和2010年三期7月—9月的Quick Bird影像的精度分析结果发现:研究区森林变化检测的总体精度可达96.53%,Kappa系数为0.9512。在添加NDVI指数后构建的IFZ提高了总体监测精度。其中,毁林类别的检测精度提高显著,漏检率和误检率分别为2.74%和3.64%;干扰后重建的森林类别的检测精度有一定提高,其漏检率和误检率分别为10.79%和10.51%。研究结果表明,改进暗目标法能提高森林样本的选样效率,添加NDVI的IFZ能提高森林动态变化的识别度。此外,本算法不仅能定性识别森林变化,而且能定量提供森林干扰发生时间和干扰强度。     

一种顾及永久散射体的SBAS InSAR时序地表沉降提取方法

提出了一种顾及永久散射体的SBAS InSAR时序地表形变提取方法。通过设置相干性、振幅离差指数、形变速率3阈值提取稳定的永久散射体,将其作为地面控制点引入SBAS InSAR处理流程,完成对地表形变的提取,并以郑州地区为例进行试验。获取了郑州市区2016年11月至2017年11月的地表沉降速率和时序地表沉降量,通过与PS InSAR、SBAS InSAR处理结果及水准测量进行对比,说明了该方法的可靠性。     

基于多通道奇异谱的GNSS坐标序列 粗差探测与数据插值

由于接收机故障、天线更换以及一些未知外界环境因素的影响,导致GPS时间序列中不可避免地存在数据缺失和粗差,数据缺失和粗差会产生各种问题,因此需要鲁棒探测粗差和缺失数据插值来获得连续完整统一的时间序列。传统方法可能需要针对不同类型的数据和不同长度的数据间隙研究不同的插值方法,这使得缺失数据的插值较为困难。针对上述问题,文中采用多通道奇异谱分析(Multichannel Singular Spectrum Analysis,MSSA)对时间序列进行粗差探测和缺失数据插值,重建非均匀采样时间序列的连续可靠模型,且不需要关于时间序列的任何先验信息。在该方法中,粗差探测与数据插值同时进行。模拟数据和实测GNSS时间序列数据测试结果都表明该方法的有效性。     

Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring

Free and open access to the Landsat archive has enabled the implementation of national and global terrestrial monitoring projects. Herein, we summarize a project characterizing the change history of Canada’s forested ecosystems with a time series of data representing 1984–2012. Using the Composite2Change approach, we applied spectral trend analysis to annual best-available-pixel (BAP) surface reflectance image composites produced from Landsat TM and ETM+ imagery. A total of 73,544 images were used to produce 29 annual image composites, generating ～400 TB of interim data products and resulting in ～25 TB of annual gap-free reflectance composites and change products. On average, 10% of pixels in the annual BAP composites were missing data, with 86% of pixels having data gaps in two consecutive years or fewer. Change detection overall accuracy was 89%. Change attribution overall accuracy was 92%, with higher accuracy for stand-replacing wildfire and harvest. Changes were assigned to the correct year with an accuracy of 89%. Outcomes of this project provide baseline information and nationally consistent data source to quantify and characterize changes in forested ecosystems. The methods applied and lessons learned build confidence in the products generated and empower others to develop or refine similar satellite-based monitoring projects.     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.     

A cloud detection method based on a time series of MODIS surface reflectance images

The Moderate Resolution Imaging Spectroradiometer (MODIS)-Terra surface reflectance product (MOD09A1), with bands 1 to 7, is a gridded, eight-day composite product derived from the MODIS-Terra top of atmosphere reflectance swaths. It performs cloud detection and corrects for the effects of atmospheric gases and aerosols. The cloud mask (CM) algorithms for MODIS are based on empirical thresholds on spectral reflectance and brightness temperature. Since the spatial resolution of the thermal band is 1000 m, while that of MOD09A1 is 500 m, many undetected and false clouds are observed in MOD09A1. These errors always result in temporal and spatial inconsistencies in higher-level products. In this paper, a cloud detection algorithm (TSCD) based on a MOD09A1 time series is introduced. Time series cloud detection (TSCD) algorithm is based on the relative stability of ground reflectance and the sudden variations in reflectance that result from cloud cover. The algorithm first searches the clear-sky reference data, and then discriminates clouded and unclouded pixels by detecting a sudden change of reflectance in the blue wavelength and spectral correlation coefficient at the pixel level. Compared with cloud cover assessments obtained from MODIS' original CM, TSCD provides similar or better discrimination in most situations when the land surface changes slowly.     

基于时序分析的建筑物沉降数据处理

变形数据分析与预报是变形监测数据处理的重要内容。基于时间序列分析的特性,研究了应用AR模型对建筑物沉降数据进行分析处理和预报的方法步骤,并通过实例计算证明,该模型具有较好的拟合效果和预报精度。     

Reconstruction of time series MODIS EVI data using de-noising algorithms

Long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data have inherent noise due to clouds and poor atmospheric conditions that limit its applicability for environmental applications. This study was carried out with an objective of noise removal and reconstruction of time series MODIS EVI data (16 day) for the period 2010–2014 using de-noising algorithms. Relative evaluation of de-noising algorithms for smoothing temporal data with ideal noise free data is not possible in actual scenario. Hence, synthetic signals were generated and introduced Gaussian noise at different variance levels for evaluation purpose. Spatial analysis was carried out by introducing noise at different variance levels into the noise free EVI images from the raw EVI stacked image. Spatio-temporal analyses of noise signals in the reconstructed EVI images were evaluated in terms of performance indicators, namely Peak Signal-to-Noise Ratio and Mean Square Error.     

联合EMD-HD和小波分解的GNSS坐标时间序列降噪分析EI北大核心CSCD

针对经验模态分解(empirical mode decomposition,EMD)在GNSS坐标时间序列的降噪过程中存在筛选准则的选取和模态混叠效应等问题,本文引入Hausdorff距离(Hausdorff distance,HD)筛选准则并结合小波分解(wavelet decomposition,WD),提出EMD-HD&WD算法。通过对我国大陆构造环境监测网络149个GNSS测站的垂向坐标时间序列降噪处理,分别利用复合指标T值、测站的速度不确定度和闪烁噪声振幅验证算法的可靠性和普适性。结果显示:HD优于现有的筛选准则;EMD-HD&WD算法对测站的速度不确定度和闪烁噪声振幅的平均改正率均为88.4%。分析表明,本文算法能够有效识别和剔除噪声并且改善EMD的模态混叠效应,提高GNSS垂向坐标时间序列的模型精度。     

含缺值GNSS基准站坐标序列的非插值小波分析与信号提取

受多种因素影响,GNSS基准站坐标序列通常都含有缺值,传统小波分析需要对缺值数据进行内插或补零处理。本文基于小波系数与时间序列观测数据的重构关系,提出了一种非插值的二进小波变换的最小范数解法,导出了相应的计算式,并严格证明了传统的补零处理算法与本文的最小范数解法等价。最后利用中国地壳运动观测网络一期27个基准站实测数据以及模拟数据进行了验证分析。结果表明,本文的非插值算法与插值算法提取的信号差异较小,27个基准站坐标序列的平均残差中误差仅相差2.01%(North),0.54%(East)和1.26%(Up),两种算法提取的信号之差与信号平均方差比仅相差1.16%(North),0.54%(East)和1.62%(Up)。     

基于 DTW 距离的时序 NDVI 数据植被信息提取--以秦巴山区为例

秦巴山区是我国重要的生态屏障,对该区的植被信息提取开展研究,可为区内生态服务功能及自然资源开发利用提供基础数据。通过加窗处理改进DTW距离相似性算法,结合临近度模糊分类方法对2005—2014年的MODIS NDVI时序数据进行植被信息提取。首先利用S-G滤波对MODIS NDVI时序数据进行重建;再利用2013年的采样数据构建各类植被的标准NDVI时序曲线,逐像元计算与标准NDVI时序曲线的加窗DTW距离,利用临近度模糊分类实现植被信息提取;最后验证提取精度。结果表明,算法具有较高的运行效率,可避免错误匹配,以较高的精度(总体精度83.8%,kappa系数0.77)实现长时间序列的植被信息提取。     

Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data

This paper introduces a novel methodology for generating 15-day, smoothed and gap-filled time series of high spatial resolution data. The approach is based on templates from high quality observations to fill data gaps that are subsequently filtered. We tested our method for one large contiguous area (Bavaria, Germany) and for nine smaller test sites in different ecoregions of Europe using Landsat data. Overall, our results match the validation dataset to a high degree of accuracy with a mean absolute error (MAE) of 0.01 for visible bands, 0.03 for near-infrared and 0.02 for short-wave-infrared. Occasionally, the reconstructed time series are affected by artefacts due to undetected clouds. Less frequently, larger uncertainties occur as a result of extended periods of missing data. Reliable cloud masks are highly warranted for making full use of time series.     

局部均值分解和奇异值分解在GNSS站坐标时间序列信号降噪中的应用

为了有效地提取GNSS站坐标时间序列的有用信息，降低噪声干扰，本文提出一种局部均值分解和奇异值分解相结合的信号降噪方法，并利用5个测站的实测坐标时间序列对新方法进行了验证。首先通过局部均值分解将坐标时间序列分解成一系列PF分量和余项，然后利用连续均方误差方法确定高频分量与低频分量的分界点，保持低频分量不变，运用奇异值分解方法对高频分量进行降噪重构，最后将重构的高频分量与低频分量叠加得到最终的降噪坐标时间序列，并对降噪效果进行对比分析。结果表明，与单纯的奇异值分解方法相比，局部均值分解和奇异值分解相结合方法能够自适应地选择合适的奇异值个数进行信号重构，提高了降噪效果。     

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m² area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R^2?=?0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R^2?=?0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms.