Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

CBERS-02星图像几何纠正方法试验研究

为了研究中巴地球资源卫星CCD多光谱数据规模化应用的可行性,扩大基础地理信息更新的影像资料来源,以CBERS-02星（简称02星）CCD多光谱数据的标准景为例,对2种几何纠正模型进行了对比研究,分析了3种不同基础控制资料对纠正精度的影响,并对精度评价方法进行了试验研究。在此基础上,总结形成了针对02星CCD多光谱数据1∶10万比例尺影像最为省时有效、精度最佳的几何纠正技术方法。     

国土资源卫星遥感数据应用评估系统构建

从国土资源应用出发,在“指标-模拟-评估-指标修改-模拟-评估”模式的基础上,构建了由软件系统、硬件系统、技术标准和技术方法4部分组成的评估系统,建立了较为完整的卫星数据应用评价技术方法体系,为实现在卫星研制初期就真正开展天地一体的我国卫星研制和应用的发展路线,提供了新的思路和技术体系支撑。     

月地岩矿光谱特征对比及月表信息提取方法简介

分析了月、地同类岩石矿物的光谱特征差异,对比了遥感地质信息提取方法和模型的适用性。利用Clemen-tine UV-VIS（紫外—可见光）数据,以月表典型撞击坑Aristillus（阿里斯基尔）和典型月海Humorum（湿海）为研究区,对月球表面岩矿信息提取方法进行了初步实践。     

基于模板阴影体算法的矢量数据在三维场景中的绘制

提出一种基于模板阴影体算法的矢量数据绘制方法,实现矢量数据在三维场景中的高质量实时叠加显示。南于该方法基于屏幕空间,所以具有像素级的精度,不会出现传统的基于纹理方法所产生的绘制走样现象;而且不受地形几何数据的约束,其执行效率与地形数据的复杂度无关,仅取决于矢量数据本身的复杂度。详细论述基于模板阴影体算法矢量数据绘制的关键技术,并通过试验验证该方法的有效性。     

基于全站仪观测数据的三维平差模型及数据处理

建立基于全站仪多维观测数据的三维网平差模型,用VisualBasic语言编制计算程序,使其自动完成控制网的平差,解决实际应用中全站仪采集数据和成果处理中存在的问题,指导测绘生产,使全站仪更方便地用于地形控制测量、工程控制测量等测绘工程中。     

空间信息网络服务中的数据安全策愀

以地理信息网络服务的安全策略为研究对象,根据我国空间数据生产到应用整个流程保密管理规定,研究地理空间数据在存储、分发、传输、表现以及与其他系统集成应用阶段上的安全策略,解决地理空间数据共享与空间数据安全之间的矛盾。     

基于中间件的遥感影像数据共享技术研究

针对遥感影像共享服务的应用需求,提出基于中间件技术的影像数据共享方法;设计基于中间件技术的影像数据服务模式。通过实验实现支持多用户并发访问的服务原型,验证基于中间件技术的影像数据服务方法的可行性并给出进一步研究的方向。