From 2D TO 3D Through Modelling Based On A Single Image

Three-dimensional modelling from single images remains an interesting topic of investigation in the research community, even though range sensors are becoming a common alternative for the generation of 3D information. The interest in single-image-based modelling is motivated by a wide spectrum of applications such as cultural heritage, civil engineering, urban planning and even criminology. In this paper a complete new production flowline is presented for modelling based on a single image. The modelling process consists of a series of familiar steps in photogrammetry and computer vision: feature extraction, vanishing point computation, camera self-calibration, 3D reconstruction and dimensional analysis. In particular, the methodology developed for single-image-based modelling takes a scientific approach combining several proven techniques with robust estimators. Finally, in order to demonstrate its capabilities, the reported examples include several real situations applied in different contexts.     

Accuracy assessment of lidar-derived digital elevation models

Despite the relatively high cost of airborne lidar-derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal distribution of the derived height differences or errors. This paper proposes a new methodology to assess the vertical accuracy of lidar DEMs using confidence intervals constructed from a finite sample of errors computed at check points. A non-parametric approach has been tested where no particular error distribution is assumed, making the proposed methodology especially applicable to non-normal error distributions of the type usually found in DEMs derived from lidar. The performance of the proposed model was experimentally validated using Monte Carlo simulation on 18 vertical error data-sets. Fifteen of these data-sets were computed from original lidar data provided by the International Society for Photogrammetry and Remote Sensing Working Group III/3, using their respective filtered reference data as ground truth. The three remaining data-sets were provided by the Natural Environment Research Council's Airborne Research and Survey Facility lidar system, together with check points acquired using high precision kinematic GPS. The results proved promising, the proposed models reproducing the statistical behaviour of vertical errors of lidar using a favourable number of check points, even in the cases of data-sets with non-normally distributed residuals. This research can therefore be considered as a potentially important step towards improving the quality control of lidar-derived DEMs.     

基于本体的地理空间信息集成框架与实现

论述了地理本体的基本概念、结构,介绍了地理本体的建模方法,提出了基于地理本体的空间数据集成框架,包括构建地理本体、数据预处理、概念映射、数据映射和数据抽取与转换5个步骤;设计了基于本体的空间数据集成方案,以数字地形图数据、海图数据向标准本体的集成为例对方案进行验证试验,并取得了初步成果。     

提高GPS RTK拟合高程精度的试验研究

通过对工程实例的分析、总结,提出了在小范围平坦地区提高GPSRTK拟合高程的精度的一种方法,该方法在生产中具有一定的实用价值。     

基于免疫多智能体的网络入侵主动防御模型

构建了一种基于免疫多智能体的网络入侵主动防御模型ADNII MA(the active defense model for net-workintrusion based oni mmune multi-agent),提出免疫智能体概念,建立免疫智能体的逻辑结构及其运行机制,实现了对网络入侵的多层次、分布式主动防御机制,为网络安全保障提供一种新的思路。     

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。     

PS-DInSAR公共主影像的优化选取

在充分顾及DInSAR时空基线跨度及多普勒质心频率差异3个因素对干涉相关性影响的基础上,提出一种PS—DInSAR公共主影像的优化选取算法,采用综合相关测度作为总体干涉相关性的衡量指标,给出具体的综合相关函数模型及其求解方法。借助欧洲空间局（ESA）ERS-1／2卫星从1992—2002年间飞越上海地区所获取的25幅C波段SAR影像作为源数据,开展了PS—DInSAR公共主影像的优化选取实验。计算结果与分析表明,综合相关系数模型用于PS公共主影像的优化选取是有效的和可靠的。