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对城市空间数据基础设施数据的尺度和精度进行了研究,分析了衡量城市空间数据尺度的4个指标:比例尺、范围、详尽程度和分辨率,总结了现有测量规范对主要城市空间基础数据的精度要求,论述了不同尺度和精度条件下城市空间基础数据的协调,提出了数字城市空间基础数据的平面精度分级体系,建立了此分级体系与传统测量精度指标及比例尺的对应关系。 相似文献
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基于自然间断点分级法的土地利用数据网格化分析 总被引：4，自引：0，他引：4
土地利用在自然资源统一管理中扮演着重要角色，面对不同区域和年份的数据，统一分析比对口径尤为重要，同时也应反映出相互之间的差异。本文以宜兴市2009年和2017年土地利用现状数据为数据源，首先使用统一的分类标准提取用地类型中的3大类，通过不同大小的单元划分尝试和结果分析，发现适用于该数据的网格尺度大小；然后基于自然间断点分级法进行分级范围划定，对宜兴市三类用地类型的分布和变化趋势进行综合分析，较为真实地反映了宜兴市用地情况；最后通过选用合适的空间尺度和分级范围划定方法，进而构建一个兼具操作性和科学性的土地利用数据网格化方法，为自然资源部门统筹管理和综合治理提供依据。 相似文献
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多源遥感数据综合应用是遥感发展的必然趋势,统一的遥感数据空间尺度分级模型是多源数据集成与综合应用的基础。虽然已有多种空间尺度分级模型,但很多主流模型并非出于分尺度综合应用目的,缺乏客观的比较和评价。国家基本比例尺系统作为经过论证、中国应用面最广泛、接受度最高的一种尺度分级系统,是以应用为导向的遥感数据空间尺度分级模型的最优参照系。从不同视觉精度下国家基本比例尺对图像空间分辨率的需求出发,比较各空间尺度分级模型的层级分辨率与需求分辨率的匹配情况,包括OGC Well Known Scale Set的Global CRS84Pixel和Google Maps Compatible,以及NASA World Wind、Google Map、百度地图、天地图等软件平台采用的层级格网系统,以及"五层十五级"遥感数据组织模型,通过对数据信息冗余度的分析,对各个模型进行了评价。结果表明,在高视觉精度应用需求下,"五层十五级"模型与基本比例尺精度要求具有较明显的匹配优势,其次为OGC Google Maps Compatible模型和天地图模型,其余模型平均数据冗余倍数在2倍左右;在低视觉精度应用需求下,"五层十五级"模型平均数据冗余度仍为最低,其次为Google Map模型,其余模型平均数据冗余倍数都在2倍以上。 相似文献
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为满足用户多样化的需求,需要基于1:50000地形要素数据构建标准分级产品.由于该数据中的居民地数据本身不含级别信息,因此提出基于Voronoi图的应用开方根规律的居民地分级方法,该疗法可以保证分级后居民地的空间分布特征与原始数据一致,准确反映居民地分布的疏密对比与聚集状态,能够为基于1:50000地形要素数据的标准分级数据产品的设计加工提供较好的基础. 相似文献
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传统的统计方法对国内生成总值（GDP）进行统计耗时耗力费财。而夜间灯光遥感影像能对人类社会活动进行最直观的反映，为社会经济研究提供了新的思路方法和技术支持。本文以山东省作为研究区域，采用NPP-VIIRS灯光影像数据与山东省137个县级GDP数据进行分级多方式空间建模，以找到两者空间分布的最优规律。试验结果表明总GDP与总灯光亮度值的各级最优拟合优度R2均在0.9左右。单位面积GDP与平均灯光亮度值的各级拟合优度R2均在0.85左右。而且通过最优模型进行县级和市级GDP预测精度优于未分级的预测精度。 相似文献
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地形地貌随着时间的推移时刻发生变化，以高精度数字高程模型（EDE）数据制作坡度图，进而计算田坎系数，更精准地进行耕地面积计算及统计。以2 m格网点云数据生成的DEM数据为基础，按照第三次全国国土调查的相关技术要求，根据耕地坡度分级要求进行分级，生成坡度分级栅格数据图。对坡度分级栅格数据图进行矢量化，生成坡度分级矢量化数据。对矢量化数据进行图斑综合、界线平滑、拓扑重建、数据裁切等处理，制作完成调查区域坡度图及相关属性数据制作。并对生产的坡度图成果进行分析，找出不同尺度格网生产的坡度图的技术差异，并对高精度坡度的应用进行了展望。 相似文献
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An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings 总被引：2，自引：0，他引：2 
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下载免费PDF全文 Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling‐based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets. 相似文献
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S. Biswal A. Ghosh R. Sharma P. K. Joshi 《Journal of the Indian Society of Remote Sensing》2013,41(3):523-530
In this study we explored the potential of open source data mining software support to classify freely available Landsat image. The study identified several major classes that can be distinguished using Landsat data of 30 m spatial resolution. Decision tree classification (DTC) using Waikato environment for knowledge analysis (WEKA), open source software is used to prepare land use land cover (LULC) map and the result is compared with supervised (maximum likelihood classifier – MLC) and unsupervised (Iterative self-organizing data analysis technique - ISODATA clustering) classification techniques. The accuracy assessment indicates highest accuracy of the map prepared using DTC with overall accuracy (OA) 92 % (kappa = 0.90) followed by MLC with OA 88 % (kappa = 0.84) and ISODATA OA 76 % (kappa = 0.69). Results indicate that data set with a good definition of training sites can produce LULC map having good overall accuracy using decision tree. The paper demonstrates utility of open source system for information extraction and importance of DTC algorithm. 相似文献
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Earthquake, flood, and drought data from different sources are combined in a single data set using the same data structure, projection, and scale. The intensity and frequency of each hazard is classified into severe, heavy, modest, and light, producing a classification with 64 combined states for the three kinds of hazard. These classes are then ranked according to severity. The three hazard coverages arc overlaid and the polygons that are produced are coded by the classification system. A map is produced that shows the distribution of these 64 classes in regions and their areas measured from the spatial topological data file in the GIS. Spatial analysis reveals the spatial association among the three hazards and between the three hazards and human factors. There is a brief discussion of the implications of the regionalized map for hazard monitoring. 相似文献
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Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model 总被引：1，自引：0，他引：1
Yichun Xie Zongyao Sha Yongfei Bai 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(2):182-190
Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model. 相似文献
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一种简单加入空间关系的实用图像分类方法 总被引：10，自引：0，他引：10
遥感图像分类是遥感图像处理的一项基本内容，也是遥感应用中关键的一步。为了提高分类的精度，一方面是对光谱信息的合理利用；另一方面，可以加入新的信息源，即进行多源数据处理，并加入地学知识，尤其是对空间信息的利用是至关重要的。但是由于地学知识的复杂性及空间信息利用的难度以及数据源的限制，尚无公认的实用方法。该文提出了一种简单加入空间关系的分类方法，在没有其它数据源的情况下，利用空间关系特性，在分类中构造两个空间关系波段，实现空间约束，部分消除仅依赖光谱数据分类而引起的同物异谱和同谱异物造成的分类错误。简单实用，同时也验证了空间关系在分类中的重要性。 相似文献
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基于ETM＋制作土地利用覆盖图——以制作北京1：5万土地利用覆盖图为例 总被引：3，自引：1，他引：2
简要介绍了基于LANDSAT7 ETM＋影像,采用计算机非监督分类、监督分类与人工解译相结合的方法制作土地利用覆盖图的过程和所采用的关键技术,给出了适用于规模化生产土地利用覆盖数据的工艺流程图。使用该方法制作的十一种分类要素的北京地区1：5万土地利用覆盖图,平均分类精度为84．85％,可以满足一般用户对土地利用覆盖图的要求。 相似文献
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地图自动综合知识的分类及其形式化描述 总被引：1，自引：0，他引：1
在分析了已有地图综合知识的分类和表示方法的基础上 ,提出了一种新的地图综合知识的分类方法和形式化方法 ,这种分类方法易于系统开发 ,其形式化方法便于同地图综合模型和空间数据管理相结合。 相似文献
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Richard Lucas Katie MedcalfAlan Brown Peter BuntingJohanna Breyer Dan Clewley Steve KeyworthPhilippa Blackmore 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(1):81-102
The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-orientated rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land. 相似文献
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This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products. 相似文献