Towards 3D map generation from digital aerial images

This paper describes the fusion of information extracted from multispectral digital aerial images for highly automatic 3D map generation. The proposed approach integrates spectral classification and 3D reconstruction techniques. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and near infrared (NIR) channels and form the basis for information extraction.Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model (DSM) for a refined classification. The DSM is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature-based points of interest the algorithms are able to generate a dense DSM by a dense image matching procedure. Afterwards a true ortho image for classification, panchromatic or color input images can be computed.In a last step specific layers for buildings and vegetation are generated and the classification is updated.     

利用IRS-P6卫星数据制作遥感影像地图

本文介绍了以IRS-P6卫星数据作为信息源完成大兴安岭林区的遥感影像地图制作的方法, 通过试验初步认识到:利用IRS-P6卫星数据制作遥感影像地图,可以满足1:50000、 1:25000比例尺的平面制图精度要求,在一些专业和专题应用中可以放大到1:15000比例尺使用。     

基于同名点对的小面元影像微分纠正及融合方法

多源遥感影像数据融合是提高海量遥感影像数据利用率，获取更多决策辅助信息的有效手段之一。目前一些商业遥感图像处理软件在进行多源遥感影像融合时，存在效率低、精度难以达到实用要求等问题。本文提卅了基于图像线特征的遥感影像精确配准、纠正、融合新方法，该方法运用线特征算法提取图像边缘特征点作为控制点，然后通过全自动图像匹配获取密集同名点对，由这些同名点对构成密集三角网（小面元）实现影像精确配准；最后进行影像纠正融合处理。大量实践表明，该方法是一种高效实用、高精度的遥感影像配准、纠正、融合方法，能大大改善山区等特殊地区的配准精度。     

基于分布式计算模式的GIS的设计与实现

介绍了分布式GIS和分布式计算模式的概念,探讨了基于分布式计算模式的地理信息系统的体系结构、运作机制、关键技术和实现方法,最后对未来的分布式地理信息系统发展进行了展望。     

基于数据融合的SRTM数据空洞填补方法

总结国内外对SRTM数据空洞的填补方法,指出基于内插的填补方法无法满足大区域SRTM数据处理的需求。提出在内插的基础上进行数据融合的空洞填补方法,并以实例验证了该方法是一种获取完整地形数据的有效途径。     

浅谈多传感器信息融合技术

多传感器信息融合广泛应用于自动目标识别、战场监视、机器人、工业过程控制、遥感、图象处理、模式识别等领域。介绍了多传感器信息融合技术的概念、原理、功能模型,其融合方法主要介绍了Dempster-Shafer证据理论(D-S证据理论),由于D-S证据理论存在处理大量不确定的系统估计问题的特性,可以将其应用于势态估计问题。     

Analysis of Fog Probability from a Combination of Satellite and Ground Observation Data

The Cloud Type product, developed by the Satellite Application Facility to support to nowcasting and very short-range forecasting (SAFNWC) of EUMETSAT and based on Météosat-8/SEVIRI, identifies cloud categories, and especially low and very low clouds which are first estimates of areas where fog is likely to occur. This cloud type is combined with precipitation information from radar data and with hourly diagnostic analyses of 2-metre relative humidity and 10-metre wind to elaborate an hourly analysis of fog probability. This analysis provides four levels of fog probability with a 3-kilometre horizontal resolution: No risk, low-level risk, medium-level risk and high-level risk. An evaluation of such fog probability analyses versus a one-year set of French hourly SYNOP reports shows encouraging results (potential of detection = 0.73 for low and medium and high-level risks), even if false alarm ratios remain high. Most of the non-detections occur at twilight and are due to satellite non-detections. Eventually, we show case studies that clearly illustrate the high potential of the method.     

A level-set corrector to an adaptive multiscale permeability prediction

We consider the inverse problem of permeability estimation for two-phase porous-media flow. The novel approach is based on regularization by zonation, where the geometry and size of the regions are chosen adaptively during the optimization procedure. To achieve this, we have utilized level-set functions to represent the permeability. The available data are sparsely distributed in space; hence, it is reasonable to confine the estimation to coarse-scale structures. The level-set approach is able to alter the boundaries between regions of different permeability without strict restrictions on their shape; however, when the data are sparse, a reasonable initial guess for the permeability is required. For this task, we use adaptive multiscale permeability estimation, which has the potential of identifying main permeability variations. These are described by a piecewise constant function, where the constant values are attained on rectangular zones. In the current work, we develop a level-set corrector strategy, assuming adaptive multiscale permeability estimation as a predictor.     

基于à trous小波变换的Landsat 7 ETM＋图像融合研究

 以胶州湾及周边海岸带为研究区，采用Landsat 7 ETM+数据，提出一种基于à trous小波变换的全色图像和多光谱图像融合改进算法。对全色图像和多光谱图像进行适当层数的小波分解，多光谱图像的低频部分采用全色图像和其低频分量的比来调制； 最高分解层外的其余分解层采用多光谱图像和全色图像在该层分解系数的加权和，加权系数由局部区域能量比来确定； 最高分解层则采用绝对值最大准则。实验表明，该方法得到的图像可提高空间分辨率，对多光谱图像的光谱信息扭曲也较小，为提高海岸带地物分类和信息提取精度奠定了基础。     

Comparison of Two Fading Filters anc Adaptively Robust Filter

Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.