基于空间方向相似性的面状矢量空间数据匹配算法

数据一致性匹配处理技术是空间数据融合中的关键技术。采用方向关系矩阵模型进行空间方向关系描述,讨论了一般情况下的空间方向相似性计算,提出了基于空间方向相似性的矢量数据匹配算法流程。最后,以面状目标为研究对象,以某地局部地区居民地数据为例,对基于空间方向相似性的矢量数据匹配算法进行了试验与分析。     

一种新的SAR影像融合滤波方法

为解决有效地去除斑点噪声和保留图像边缘两者矛盾的问题,首先通过中值滤波得到较好的平滑SAR图像,然后利用AMSS方程得到较好的保留图像边缘信息的SAR图像,最后将所得的两幅图像进行融合,并对融合后的图像进行定量和定性分析。仿真实验表明,无论是定性分析,还是定量分析都证明融合后的图像在很好地去除噪声的同时,有效地保留了图像的地物分界处及边缘细节信息。     

数字正射影像图在土地利用动态监测中的应用

以山东省潍坊市为例,介绍了利用法国SPOT5 2.5 m全色和10 m多光谱数据制作正射遥感影像图的方法,以及利用两期数字正射影像图进行土地利用动态监测的方法.并对潍坊市2004年度变化监测结果进行了分析.     

Variational data assimilation for deriving global climate analyses from GNSS radio occultation data

A comprehensive global navigation satellite system (GNSS) based radio occultation (RO) data set is available for meteorology and climate applications since the start of GNSS RO measurements aboard the CHAllenging Mini-satellite Payload (CHAMP) satellite in February 2001. Global coverage, all-weather capability, long-term stability and accuracy not only makes this innovative use of GNSS signals a valuable supplement to the data set assimilated into numerical weather prediction (NWP) systems but also an excellent candidate for global climate monitoring. We present a 3D variational data assimilation (3D-Var) scheme developed to derive consistent global analysis fields of temperature, specific humidity, and surface pressure from GNSS RO data. The system is based on the assimilation of RO data within 6 h time windows into European Centre for Medium-Range Weather Forecasts (ECMWF) short-term (24 h, 30 h) forecasts, to derive climatologic monthly mean fields. July 2003 was used as a test-bed for assessing the system’s performance. The results show good agreement with climatologies derived from RO data only and recent NWP impact studies. These findings are encouraging for future developments to apply the approach for longer term climatologic analyses, validation of other data sets, and atmospheric variability studies.     

Improved Weather Radar Echo Extrapolation Through Wind Speed Data Fusion Using a New Spatiotemporal Neural Network Model

Weather radar echo extrapolation plays a crucial role in weather forecasting. However, traditional weather radar echo extrapolation methods are not very accurate and do not make full use of historical data. Deep learning algorithms based on Recurrent Neural Networks also have the problem of accumulating errors. Moreover, it is difficult to obtain higher accuracy by relying on a single historical radar echo observation. Therefore, in this study, we constructed the Fusion GRU module, which leverages a cascade structure to effectively combine radar echo data and mean wind data. We also designed the Top Connection so that the model can capture the global spatial relationship to construct constraints on the predictions. Based on the Jiangsu Province dataset, we compared some models. The results show that our proposed model, Cascade Fusion Spatiotemporal Network (CFSN), improved the critical success index (CSI) by 10.7% over the baseline at the threshold of 30 dBZ. Ablation experiments further validated the effectiveness of our model. Similarly, the CSI of the complete CFSN was 0.004 higher than the suboptimal solution without the cross-attention module at the threshold of 30 dBZ.     

面向多源地理空间数据的知识图谱构建

知识图谱广泛应用于人工智能领域,基于此融合多源地理空间数据并表示地理事物的语义和时空信息,实现“数据—知识”的转换成为人们关注的热点。但现有通用知识图谱的空间知识覆盖度低且存在错误,同时基于维基百科构建的地理知识图谱存在空间关系、中文属性和坐标信息等属性缺失问题。因此本文以地理空间数据和百度百科数据的特征分析为基础,提出了以地理空间数据提取地理实体为主,百度百科补充属性信息为辅的知识图谱构建方式。① 基于GeoSparql设计模式层的地理实体、要素、几何形状和空间关系的逻辑关系;② 通过地理实体提取、实体链接和属性信息填充,在数据层实现空间知识融合;③ 结合关系型数据库和图数据库,设计空间知识存储方式;④ 在实体和关系2个方面定量分析知识图谱的构建规模。结果表明,本文构建的知识图谱中地理实体覆盖度和链接百科成功率相对较高,扩充了地理实体的概念描述信息,并将地理坐标的覆盖率提高到100%,对地理数据到地理知识的拓展具有重要意义。     

基于多库融合的地理信息数据生产体系的研究

由于地理信息数据种类的不断增加,数据的孤立存储、管理、生产、更新的弊端也日益突显;统一管理、联动更新将极大节约数据生产成本,提高数据生产更新效率。本文提出的基于多库融合的地理信息数据生产体系,是对多源数据充分分析的基础上通过制定规则实现数据的统一管理、同步更新的地理信息数据生产新思路、新方法,是测绘数据管理和生产方式的大胆创新。     

基于VirtuoZo和ERDAS的地形三维信息的提取与可视化

大范围地形三维信息的提取和场景可视化是当前地理信息产业研究的热点,本文使用适普软件公司的VirtuoZo全数字摄影测量系统,从航片等数据中提取地形三维信息,并使用ERDAS处理该地区的多源影像数据,最后,以处理后的多源融合影像为纹理,实现大范围空间的地形三维可视化。     

A novel image-fusion method based on the un-mixing of mixed MS sub-pixels regarding high-resolution DSM

A major reason for the spectral distortions of fused images generated by current image-fusion methods is that the fused versions of mixed multispectral (MS) sub-pixels (MSPs) corresponding to panchromatic (PAN) pure pixels remain mixed. The MSPs can be un-mixed spectrally to pure pixels having the same land cover classes in a fine classification map during the fusion process. Since it is difficult to produce such a land cover classification map using only MS and PAN images, a Digital Surface Model (DSM) derived from airborne Light Detection And Ranging data were employed in this study to facilitate the classification. In a novel fusion method proposed in this paper, MSPs near and across boundaries between vegetation and non-vegetation are identified using MS, PAN, and normalized Digital Surface Model (nDSM). The identified MSPs then are fused to pure pixels with respect to the corresponding land cover class in the classification map. In a test on WorldView-2 images over an urban area and the corresponding nDSM, the fused image generated by the proposed method was visually and quantitatively compared with fused images obtained using common image-fusion methods. The fused images generated by the proposed method yielded minimal spectral distortions and sharpened boundaries between vegetation and non-vegetation.     

融合深度相机点云与光学影像的室内三维建模

针对传统的对点云进行拼接建模的方法无法得到较好的室内三维模型的问题,该文提出了一种融合Kinect点云与光学影像数据的室内三维建模方法:首先,利用Agisoft Photoscan软件对Kinect获得的彩色影像进行处理获得大量三维点,并与拼接后的Kinect点云进行融合,从而弥补Kinect点云缺失严重的现象;然后,针对不同的物体利用不同的方法对融合后的点云数据进行处理,并通过"孔洞算法"提取其边界点;最后,将边界点进行处理从而完成室内三维场景模型。实验结果表明,该文方法能够提供质量较高的三维点云数据,并且能得到较好的室内三维模型。