多源遥感数据管理与远程处理集成实现

针对目前对多源遥感影像数据管理与远程处理的集成应用研究较少的现状,文章基于FTP、关系数据库和空间数据库引擎设计多源遥感影像数据及其元数据与快视图数据的一体化管理方案,基于Web Service与PCI-EASI设计遥感影像远程处理服务,并对多源遥感影像数据管理与远程处理功能进行集成,通过综合运用FTP和Web Service技术,提高了数据管理效率和应用的灵活性。最后以"粤港澳水质遥感监测系统"为例,实现了对多源遥感影像数据的有效管理及水质监测业务的系统化,验证了研究成果的可行性和实用性。     

多源海量统计遥感数据集成管理技术研究

统计遥感业务是国家统计局基于统计业务对空间信息的发展和应用需求,将GIS、Rs和GPS空间信息技术全面应用于统计数据的获取、管理与分析中,从而提高统计效率和统计科技含量,构建新型国家统计业务体系。统计遥感业务数据具有多源、海量的特点,实现统计遥感数据的集成与管理是新型统计遥感业务成功运转的基础。针对统计遥感业务及数据应用需求,通过对各类空间数据集成与管理技术进行研究,提出在建立统计遥感空间数据库基础上,将影像以县级为单位进行拼接、然后压缩存储到统计遥感空间数据库,同时结合影像编目技术实现海量遥感影像的管理;提出将元数据管理和数据转换、数据直接访问以及数据互操作方式,实现多源统计遥感数据的集成与管理。通过多种方式,从应用层面较好地解决了海量、多源统计遥感数据的集成与管理,提升了统计遥感数据的应用范围。     

基于GIS的塔里木河流域遥感影像数据库系统的建立

采用当前流行的数据仓库管理技术,按照空间数据-属性数据一体化、多源数据无缝集成及基于C/S结构和关系数据库3层体系结构的设计思想,将塔里木河流域遥感信息、空间地理信息、多专题信息、社会经济信息,以及多媒体和元数据等各类信息进行集成建库.实现多类型、多尺度的海量遥感影像、空间地理等基础数据的无缝集成和统一管理.     

面向遥感影像数据生产的多源控制点数据管理方法研究

提出一种服务遥感影像数据生产的多源控制点数据管理方法,利用元数据方式实现栅格、矢量和文本等形式多源控制点数据组织、查询及定位,并通过建立局域网内部磁盘映射的方式提供遥感影像数据生产中所需的控制点数据。试验表明该系统能满足遥感影像数据处理过程中控制点数据的自动查询和获取,提高了遥感影像数据生产效率。     

遥感影像数据库设计与实现

遥感影像数据库的设计与实现,主要实现了多源遥感影像数据的管理。通过调用GDAL库,实现多源遥感影像数据的读取,对影像数据进行分块,建立影像金字塔,实现影像数据和元数据入库,建立高效的索引机制;根据窗口显示范围,调取合理的金字塔层数据,并对数据进行缓冲和实施处理显示;最后,设计元数据的管理。最终实现影像数据和元数据信息的一体化管理。     

遥感影像数据库设计与实现

遥感影像数据库的设计与实现,主要实现多源遥感影像数据的管理.通过调用GDAL库,实现多源遥感影像数据的读取,对影像数据进行分块,建立影像金字塔,实现影像数据和元数据入库,建立高效的索引机制;根据窗口显示范围,调取合理的金字塔层数据,并对数据进行缓冲和实施处理显示;最后,设计元数据的管理.最终实现影像数据和元数据信息的一体化管理.     

高空间分辨率遥感影像建筑物提取方法综述

高空间分辨率光学遥感影像中建筑物的提取对城市空间数据库的更新、城市动态监测以及建设"智慧城市"等方面具有极其重要的意义。本文首先介绍了高空间分辨率遥感影像中建筑物的特点,然后介绍了基于高空间分辨率遥感影像中建筑物自动提取的四种主要方法,即多尺度分割提取方法;基于边缘和角点检测与匹配的提取方法;基于区域分割的提取方法和基于数学工具;新理论以及多种方法结合的提取方法,分析讨论了各种方法的研究进展以及各方法的优缺点,同时对建筑物提取研究中需要解决的问题和研究趋势进行了总结。最后对高空间分辨率遥感影像建筑物提取研究前景进行了展望,认为可以从多源或多时相数据、边缘检测以及阴影辅助作用等方面提取建筑物,提高建筑物提取精度和高分遥感影像的利用率。     

基于空间数据库的多源异构卫星数据一体化管理研究

我国已经成为卫星遥感数据大国,但是在多源异构海量遥感数据的系统建设和技术研发方面,现行的遥感数据存储、处理以及服务技术将面临很大的挑战。本文基于空间数据库的空间对象模型和关系模式,设计了一套"空间-属性-体化"的多源异构卫星数据标准化数据结构,开发了多源异构卫星数据一体化管理应用原型系统,实现了多源异构卫星数据的自动化入库、统一查询检索和多样化处理等功能,为多源异构卫星影像数据及其辅助元数据的一体化管理提供解决方案。     

T级影像数据管理平台的设计与实现

栅格数据如遥感影像数据、DEM数据等是重要的空间信息数据源。本文在分析了空间数据库存贮和管理技术基础上,基于对象关系模式的数据库技术,提出了一种支持T级数据、可扩展的、支持海量多分辨率多波段影像集成平台架构体系,并进行了多源海量影像数据库管理系统的设计与实现,对多源海量影像数据合理有效管理具有一定的意义。     

光学遥感影像与GIS数据一体化的水体分割、配准与提取方法

利用遥感影像进行自动化水体提取是一项富有挑战的任务。提出了一种GIS数据辅助下的可见光遥感影像水体自动提取方法,其核心是将影像分割、配准和变化检测集成为一体化处理流程检测水体。利用水体在遥感影像上的显著特性,首先提出了基于多尺度视觉注意模型的疑似水体区域检测;然后对影像显著区域进行水平集分割处理,通过迭代的分割和配准一体化处理不断优化直至获得最优的分割和配准结果,其中改进的形状曲线相似度特征用于约束分割结果与GIS水体对象的匹配;最后,利用基于缓冲区的变化检测方法获取变化和未变化的水体对象,同时利用已知水体辐射特征与GIS地物的空间位置关系剔除非水体对象。采用3组不同分辨率的遥感影像和不同尺度的GIS数据进行实验,结果显示了本文方法在水体自动提取和变化检测中的有效性。     

大规模影像数据库的设计与实现

本文对现有遥感影像数据管理方法进行了讨论,针对其存在的不足,沿用传统数据库技术成果,如SQL技术、索引技术,并利用SQL Server 2005简单易用,且支持二进制大对象(BLOB),提出分幅分块存储管理方式,解决数据的物理存储问题,结合金字塔技术、索引技术、JPEG压缩技术,解决了数据的快速加载问题。工程应用实际表明,该方法能够满足海量遥感影像数据的管理需求。     

A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery

A fast endmember-extraction algorithm based on Gaussian Elimination Method (GEM) is proposed in this paper under the fact that a pixel is an endmember if it has the maximum value in any spectral band of a hyperspectral image when based on linear mixing model. Applying Gaussian elimination is much like performing a lower triangular matrix to transform the hyperspectral image. As more endmembers have been extracted, fewer bands are needed to be involved in the Gaussian elimination process, thus greatly reducing the computing time. The experimental results with both simulated and real hyperspectral images indicate that the method proposed here is much faster than the vertex component analysis (VCA) method, and can provide a similar performance with VCA.     

ArcGIS ModelBuilder模型设计方法在影像解译中的应用

在影像解译的工作过程中,需要对大量的栅格和矢量数据进行处理,绝大部分的数据处理工作是一种流程化的重复操作。ModelBuilder是一个ArcGIS平台下一个用来创建、编辑和管理模型的应用程序。文中利用ModelBuilder快速地构建模型,模型根据设定的程序自动处理数据,在一定程度上提高影像解译工作的效率,保证最终成果质量。     

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.     

多样地表和大气状况下的MODIS数据云检测

云覆盖作为天气和气候变化的一个重要因子,对地表-大气能量平衡和水循环有着重要的影响,因此,快速、准确地利用卫星遥感技术检测云覆盖具有重要的实用价值和科学意义。利用卫星遥感数据,尤其是常用的Moderate Resolution Imaging Spectroradiometer(MODIS)影像数据,因其具有较高的光谱和时间分辨率,以及2330 km扫描幅宽,为大范围实时、准确地进行云检测提供了可能。目前,基于MODIS数据发展了大量的云检测方法,但因地表类型的多样性和大气状况(如空气污染和沙尘事件等)的复杂性,目前已有的云检测方法,检测精度通常具有较大的不确定性,且针对不同地表和大气状况缺乏普适性,同时也缺乏对检测精度的定量化评估。因此,本文首先比较了常用的3种云检测算法,并基于前人经验提出了两种改进方法(方法4和方法5),首先区分出云和冰雹,摒弃了不稳定的亮温波段,两种算法均适用于复杂地表和大气状况的云检测算法。结果显示,方法5可以较好地应用于基于MODIS数据的云检测,总体精度达92.6±7%,改进了现有基于MODIS数据的云检测算法;方法4平均总体精度82.9±13%,虽然精度相对较低,但云残留少,适合作为对云敏感度高的研究工作的云检测方法。     

Estimating mineral content of indigenous browse species using laboratory spectroscopy and sentinel-2 imagery

Trees provide low-cost organic inputs, with the potential to improve livelihoods for rural communities. Understanding foliar nutrients of tree species is crucial for integration of trees into agroecosystems. The study explored nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca) concentrations of nine browse species collected from the bushveld region of South Africa using wet analysis and laboratory spectroscopy in the region 400–2500 nm, along with partial least squares (PLS) regression. We further explore the relationship between canopy reflectance of Sentinel-2 image and foliar N, P, K & Ca. Laboratory spectroscopy was significant for N estimation, while satellite imagery also revealed useful information about the estimation of nitrogen at landscape level. Nitrogen was highly correlated with spectral reflectance (R² = 0.72, p < 0.05) for winter and (R² = 0.88, p < 0.05) for summer, whilst prediction of phosphorus potassium and calcium were considered not accurate enough to be of practical use. Modelling the relationship using Sentinel-2 data showed lower correlations for nitrogen (R² = 0.44, p < 0.05) and the other nutrients when compared to the dried samples. The findings indicate that there is potential to assess and monitor resource quality of indigenous trees using nitrogen as key indicator. This multi-level remote sensing approach has promise for providing rapid plant nutrient analyses at different scales.     

Estimation of forest LAI by inverting canopy reflectance models and multi-angle imagery

Computer simulation models have seldom been applied for estimating the structural and biophysical variables of forest canopy. In this study, an approach for the estimation of leaf area index (LAI) using the information contained in hyperspectral, multi-angle images and the inversion of a computer simulation model are explored. For this purpose, L-systems combined with forest growth model ZELIG were applied to render 3-D forest architectural scenarios. The Radiosity-graphics combined model (RGM) was used to estimate forest LAI from the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) data. LAI inversion was performed using the look-up table (LUT) method. The estimated LAI was evaluated against in situ LAI measurement and compared against the LAI predictions from CHRIS data obtained using the Li-Strahler geometric-optical canopy reflectance model (GOMS). The results indicated that the method used in this study can be efficient strategy to estimate LAI by RGM model inversion.     

卷积神经网络在高分遥感影像分类中的应用

针对目前应用于高分辨率遥感影像分类的常用算法,其精度已无法满足大数据环境下的分类要求的问题,该文提出了卷积神经网络分类算法。卷积神经网络模型降低了因图像平移、比例缩放、倾斜或者共他形式的变形而引起的误差。在大数据环境下,采用卷积神经网络算法对高分辨率遥感影像进行分类,避免了特征提取和分类过程中数据重建的复杂度,提高了分类精度。通过实验比对分析,证明了卷积神经网络在高分辨率遥感影像分类中的可行性及精度优势,对遥感图像处理领域等相关工作提供了参考价值。     

Rule-based classification of high-resolution imagery over urban areas in New York City

This paper describes the integration of results from different feature extraction algorithms using spectral and spatial attributes to detect specific urban features. Methodology includes segmentation of IKONOS data, computing attributes for creating image objects and classifying the objects with fuzzy logic and rule-based algorithms. Previous research reported low class accuracies for two specific classes – dark and grey roofs. A modified per-field approach was employed to extract urban features. New rule-sets were used on image objects having similar or near-similar spectral and spatial characteristics. Different algorithms using spectral and spatial attributes were developed to extract specific urban features from a time-series of Multi-Spectral Scanner (MSS) (4 m × 4 m) IKONOS data. The modified approach resulted in a remarkable improvement in the accuracy of classes that registered low spectral seperability and therefore low accuracy. The spectral and spatial based classification model may be useful in mapping heterogeneous and spectrally similar urban features.     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.