An approach to discovering multi-temporal patterns and its application to financial databases

Managerial decision-making processes often involve data of the time nature and need to understand complex temporal associations among events. Extending classical association rule mining approaches in consideration of time in order to obtain temporal information/knowledge is deemed important for decision support, which is nowadays one of the key issues in business intelligence. This paper presents the notion of multi-temporal patterns with four different temporal predicates, namely before, during, equal and overlap, and discusses a number of related properties, based on which a mining algorithm is designed. This enables us to effectively discover multi-temporal patterns in large-scale temporal databases by reducing the database scan in the generation of candidate patterns. The proposed approach is then applied to stock markets, aimed at exploring possible associative movements between the stock markets of Chinese mainland and Hong Kong so as to provide helpful knowledge for investment decisions.     

一种多时相遥感影像存储管理的新方法

针对海量遥感影像时间分辨率不断提高,多时相遥感影像存储空间耗费大、查询效率低的问题,提出一种针对多时相遥感影像数据存储管理的模型和方法。首先确定多时相遥感影像的基态影像,然后运用变化检测技术计算出不同时相间影像的差异,将差异变化值大于设定阈值的遥感影像区域进行存储,并用于对基态影像的修正。实验证明,运用该方法管理多时相遥感影像时,可有效节省存储空间。     

基于季相变化特征的撂荒地遥感提取方法研究

在我国西南地区耕种条件差,地块比较破碎,地块类型比较复杂,中低分辨率遥感数据难以满足撂荒地提取的需要。选取贵州修文县为试验区,基于高分辨率卫星遥感数据（哨兵2号）,探索单期或多期影像在中国西南地区的撂荒地检测能力,构建撂荒地遥感监测方法,为今后我国西南地区撂荒地统计调查提供参考。结合野外调查数据,在划分不同撂荒地类型基础上,综合遥感影像的光谱特征、植被指数特征以及多时相植被指数变化特征分析,优选不同类别撂荒地遥感提取敏感特征集,利用CART决策树分类方法,提取不同类型的撂荒地。结果表明：①单个时相对不同类型的撂荒地识别能力差异显著,基于单时相影像,难以开展撂荒地高精度遥感监测提取;②不同时相的植被指数变化特征对撂荒地的识别能力较强,其中比值植被指数优于差值植被指数和归一化植被指数;③以贵州修文县为例,开展了撂荒地空间分布制图及撂荒面积统计分析,修文县撂荒地面积约为6 460 hm²,占修文县耕地面积的13%;④基于多时相高分辨遥感数据,通过季相变化特征构建的撂荒地检测方法,能够满足我国西南地区撂荒地高精度遥感监测提取,为大范围撂荒地遥感调查和制图提供技术参考。     

基于多时相多光谱红外图像浅层地下目标探测

浅层地下目标影响周围区域的热物理特性,引起区域表面温度差异随时间变化的现象,对应在红外图像上则导致灰度值差异随时间变化．针对这一问题,本文研究了包含地下目标的区域温度分布的数学模型,揭示了区域温度变化和地下目标的热物理性质与埋藏状况的关系,进行求解得到区域表面温度分布的预测值．利用实际获取的多时相多光谱红外图像反演区域地表温度分布,利用多光谱图像丰富的光谱信息来反演区域表面的多时相温度分布,和预测值进行比对,使区域表面温度分布的探测值和预测值相一致的待求解参数的估计值即为地下目标的探测结果．     

Mapping megacity growth with multi-sensor data

In our increasingly urbanized world, monitoring and mapping of urban growth and thereby induced land-use and land-cover change (LULCC) is of emergent importance. Remote sensing can reveal spatio-temporal growth trajectories of cities, which again allow a thorough understanding of the impacts of urbanization on ecosystems and ecosystem services. However, the mapping of urban areas remains one of the most challenging tasks of remote sensing data analysis. This paper presents an approach to map urban growth from multi-sensoral data, exemplified for the Dhaka megacity region in Bangladesh between 1990 and 2006. The approach is globally applicable and can facilitate regional urban growth maps in arbitrary complex and dynamic environments.Dhaka's densified urban landscape, its deltaic locality and the highly dynamic monsoon-related phenology call for a sophisticated analysis approach that is able to separate intra-annual land-cover variations from actual urbanization. Imagery from the Landsat series of satellites is a great asset for such an analysis due to its synoptic coverage of large urban areas as well as its unique historical archives. In our approach, we solve problems of spectral ambiguities and seasonal phenological dynamics through incorporating multi-temporal imagery for each monitoring year (1990, 2000, and 2006) and by extending the spectral feature space with synthetic aperture radar (SAR) data. The resulting datasets are heterogeneous and comprise measurements of unequal scaling. Non-parametric classification algorithms are required to delineate multi-modal and non-Gaussian class distributions of heterogeneous as well as temporally and spectrally complex land-cover classes of interest in such an extended feature space. We therefore used a support vector machine (SVM) classifier and post classification comparison to reveal spatio-temporal patterns of urban land-use and land-cover changes. An SVM based forward feature selection procedure allowed deriving in-depth information about the individual contribution of different input bands.Our methodology delineated relevant land-cover classes and resulted in overall accuracies better than 83% for all years considered. Change analysis unveiled a profound expansion of urban areas at the expense of prime agricultural areas and wetlands. During the 1990s, change was primarily characterized by a densification of urban fabric, whereas more recent changes included vast in-filling of low lying land and an extensive industrial sprawl into Dhaka's peri-urban areas. Our multi-sensoral and multi-temporal mapping approach allowed for delineating temporally dynamic LULCC, which again allowed for an insightful characterization of land system changes in the megacity region of Dhaka.     

Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images

This paper presents a new method developed for the atmospheric correction of the images that will be acquired by the Venμs satellite after its launch expected in early 2010. Every two days, the Venμs mission will provide 10 m resolution images of 50 sites, in 12 narrow spectral bands ranging from 415 nm to 910 nm. The sun-synchronous Venμs orbit will have a 2-day repeat cycle, and the images of a given site will always be acquired from the same place, at the same local hour, with constant observation angles. Thanks to these characteristics, the directional effects will be considerably reduced since only the solar angles will slowly vary with time.The algorithm that will be implemented for the atmospheric correction of Venμs data is being developed using both radiative transfer simulations and the actual data acquired by the Formosat-2 satellite. Because of its one-day sun-synchronous repeat cycle, Formosat-2 acquires images with a sun-viewing geometry close to the one Venμs will offer. With this geometry, reflectance time series are free from directional effects on the short term, a feature which reduces the number of unknowns to retrieve. The atmospheric corrections algorithm exploits this feature and the two following assumptions:

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Aerosol optical properties vary quickly with time but slowly with location.

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Surface reflectances vary quickly with location but slowly with time.

Consequently, the top of atmosphere reflectance short term variations (10 to 15 days) are mainly due to the variations of aerosol optical properties, and it is thus possible to use these variations to characterise the atmospheric aerosols and to retrieve surface reflectances.This paper first describes the aerosol inversion method we developed and its results when applied to simulations. In the second part, we show the first tests of the method against three data sets acquired by Formosat-2 images with constant observation angles. Aeronet sun photometers measurements were available on all sites. Formosat-2 estimates of optical thickness compare favourably with Aeronet in situ measurements, leading to a noticeable improvement of the smoothness of time series of surface reflectances after atmospheric correction.     

Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks

The studies of impervious surfaces are important because they are related to many environmental problems, such as water quality, stream health, and the urban heat island effect. Previous studies have discussed that the self-organizing map (SOM) can provide a promising alternative to the multi-layer perceptron (MLP) neural networks for image classification at both per-pixel and sub-pixel level. However, the performances of SOM and MLP have not been compared in the estimation and mapping of urban impervious surfaces. In mid-latitude areas, plant phenology has a significant influence on remote sensing of the environment. When the neural networks approaches are applied, how satellite images acquired in different seasons impact impervious surface estimation of various urban surfaces (such as commercial, residential, and suburban/rural areas) remains to be answered. In this paper, an SOM and an MLP neural network were applied to three ASTER images acquired on April 5, 2004, June 16, 2001, and October 3, 2000, respectively, which covered Marion County, Indiana, United States. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination (R²) were calculated to indicate the accuracy of impervious surface maps. The results indicated that the SOM can generate a slightly better estimation of impervious surfaces than the MLP. Moreover, the results from three test areas showed that, in the residential areas, more accurate results were yielded by the SOM, which indicates that the SOM was more effective in coping with the mixed pixels than the MLP, because the residential area prevailed with mixed pixels. Results obtained from the commercial area possessed very high RMSE values due to the prevalence of shade, which indicates that both algorithms cannot handle the shade problem well. The lowest RMSE value was obtained from the rural area due to containing of less mixed pixels and shade. This research supports previous observations that the SOM can provide a promising alternative to the MLP neural network. This study also found that the impact of different map sizes on the impervious surface estimation is significant.     

基于时序遥感数据的福州市耕地非农化特征及驱动因子分析

耕地是粮食生产的基本载体,及时准确地获取耕地非农化信息,对于耕地资源管理和政策实施具有重要意义。为探究福州市近30 a耕地非农化变化规律,基于谷歌地球引擎（Google Earth Engine,GEE）和随机森林方法,利用多时相Landsat遥感影像提取了福州市1989、2000、2010和2019年耕地空间分布信息,并在此基础上利用土地转移矩阵、网格单元法和地理探测器等方法,分析了福州市耕地非农化的重要特征及其驱动因子。结果表明：(1)基于GEE平台的随机森林方法可有效提取南方多云多雨地区的耕地信息,土地利用分类总体精度高于90%,Kappa系数大于0.85;(2)福州市耕地空间分布不均匀,呈现东多西少,耕地面积随时间推移不断减少,耕地非农化呈现“快—慢—平”的特征。耕地非农化主要发生在高程100 m和坡度10°以下区域,耕地非农化类型主要为园林地和建设用地,其中西部地区主要为园林地,中东部地区为建设用地;(3)耕地非农化是由自然和社会因素共同驱动的结果,自然因素是耕地非农化的先决条件,城镇化增长率与人口数量增长率是导致耕地非农化主要驱动因素,其中城镇化增长率和第一产业比重增长率是...     

A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images

Over lands, the cloud detection on remote sensing images is not an easy task, because of the frequent difficulty to distinguish clouds from the underlying landscape, even at a high resolution. Up to now, most high resolution images have been distributed without an associated cloud mask. This situation should change in the near future, thanks to two new satellite missions that will provide optical images combining 3 features: high spatial resolution, high revisit frequency and constant viewing angles. The VENµS (French and Israeli cooperation) mission should be launched in 2012 and the European SENTINEL-2 mission in 2013. Fortunately, two existing satellite missions, FORMOSAT-2 and LANDSAT, enable to simulate the future data of these sensors.Multi-temporal imagery at constant viewing angles provides a new way to discriminate clouded and unclouded pixels, using the relative stability of the earth surface reflectances compared to the quick variations of the reflectance of pixels affected by clouds. In this study, we have used time series of images from FORMOSAT-2 and LANDSAT to develop and test a Multi-Temporal Cloud Detection (MTCD) method. This algorithm combines a detection of a sudden increase of reflectance in the blue wavelength on a pixel by pixel basis, and a test of the linear correlation of pixel neighborhoods taken from couples of images acquired successively.MTCD cloud masks are compared with cloud cover assessments obtained from FORMOSAT-2 and LANDSAT data catalogs. The results show that the MTCD method provides a better discrimination of clouded and unclouded pixels than the usual methods based on thresholds applied to reflectances or reflectance ratios. This method will be used within VENµS level 2 processing and will be proposed for SENTINEL-2 level 2 processing.     

基于多时相环境星NDVI时间序列的农作物分类研究

时相和归一化植被指数(NDVI)时间序列特征在农作物分类提取方面具有重要的应用价值。以黑龙江红星农场为研究区,利用多时相环境星HJ-1A/B CCD数据及其多期平滑重构后的NDVI时间序列曲线特征,在对象尺度上采用决策树算法开展了农作物分类研究,通过与单独利用多时相遥感数据分类结果的对比分析,研究了增加NDVI时序曲线特征对分类精度的影响。结果表明:面向对象分类方法得到的地块较为规则,平滑了地块内部同种作物间的噪声,避免了"椒盐现象",适合于我国东北地区农作物分类识别;利用NDVI时序曲线特征参与分类,增强了不同作物之间的光谱差异,提高了作物分类精度,比仅使用3个多时相HJ-1A/B CCD数据分类精度提高了5.45%,Kappa系数提高了0.09。通过该研究探讨了NDVI时序曲线特征在作物分类中的应用,拓展了遥感数据在农业领域的应用范围,具有推广价值。