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1.
《制图学和地理信息科学》2013,40(4):259-260
We introduce and test an algorithm for extracting high-point locations from statistical surface data. The algorithm uses map algebra and local neighborhood analysis via three key parameters: minimum vertical gain, vertical gain neighborhood, and horizontal separation neighborhood. Though the method is applicable to any x,y,z data set, we tested it on 1:250,000 digital elevation models (DEMs) for Arizona. The resulting high points were compared quantitatively with an independent data set of named summits from the USGS Geographic Names Information System (GNIS). The comparison showed that, on an aggregate basis, the extraction method can approximate the number and spatial pattern of high points when compared to the GNIS points. However, extraction by neighborhood analysis may consistently misdiagnose certain features, such as the edges of troughs (e.g., canyon rims). 相似文献
2.
The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm. 相似文献
3.
Geographic object-based image analysis (GEOBIA) produces results that have both thematic and geometric properties. Classified objects not only belong to particular classes but also have spatial properties such as location and shape. Therefore, any accuracy assessment where quantification of area is required must (but often does not) take into account both thematic and geometric properties of the classified objects. By using location-based and area-based measures to compare classified objects to corresponding reference objects, accuracy information for both thematic and geometric assessment is available. Our methods provide location-based and area-based measures with application to both a single-class feature detection and a multi-class object-based land cover analysis. In each case the classification was compared to a GIS layer of associated reference data using randomly selected sample areas. Error is able to be pin-pointed spatially on per-object, per class and per-sample area bases although there is no indication whether the errors exist in the classification product or the reference data. This work showcases the utility of the methods for assessing the accuracy of GEOBIA derived classifications provided the reference data is accurate and of comparable scale. 相似文献
4.
Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of such supervised classification techniques in GEOBIA. However, these studies did not systematically investigate all relevant factors affecting the classification (segmentation scale, training set size, feature selection and mixed objects). In this study, statistical methods and visual inspection were used to compare these factors systematically in two agricultural case studies in China. The results indicate that Random Forest (RF) and Support Vector Machines (SVM) are highly suitable for GEOBIA classifications in agricultural areas and confirm the expected general tendency, namely that the overall accuracies decline with increasing segmentation scale. All other investigated methods except for RF and SVM are more prone to obtain a lower accuracy due to the broken objects at fine scales. In contrast to some previous studies, the RF classifiers yielded the best results and the k-nearest neighbor classifier were the worst results, in most cases. Likewise, the RF and Decision Tree classifiers are the most robust with or without feature selection. The results of training sample analyses indicated that the RF and adaboost. M1 possess a superior generalization capability, except when dealing with small training sample sizes. Furthermore, the classification accuracies were directly related to the homogeneity/heterogeneity of the segmented objects for all classifiers. Finally, it was suggested that RF should be considered in most cases for agricultural mapping. 相似文献
5.
《制图学和地理信息科学》2013,40(2):100-109
Current standards for federal mapping call for use of the Geographic Names Information System (GNIS) point layer for placement of United States populated place labels. However, this point layer contains limited classification information and hierarchy information, resulting in problems of map quality for database-driven, multi-scale, reference mapping, such as maps served by The National Map Viewer from USGS. Database-driven mapping often relies simply on what labels fit best in the map frame. Our research investigates alternative sources for labeling populated places, including polygons defined by the U.S. Census Bureau, such as incorporated place, census designated place (CDP), and economic place. Within each of these polygon layers we investigate relevant attributes from the decennial and economic censuses, such as population for incorporated places and CDPs, and the number of employees for economic places. The data selected are available for the entire country to serve national mapping requirements. This combination of data allows a more refined classification of populated places on maps that better represents relative importance. Visual importance on maps through scale should derive from more than simply residential population, but also economic importance, though comparison is made to this simpler case. We differentiate a fourth category of GNIS populated place points, essentially “neighborhoods” and related features—which are not incorporated places, CDPs, nor economic places. Populated places in this fourth class do not have federally defined boundaries, necessitating an alternative method for determining hierarchy in label presentation through scale. 相似文献
6.
An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) 
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下载免费PDF全文 Spatial modeling methods usually use pixels and image objects as fundamental processing units to address real‐world objects, geo‐objects, in image space. To do this, both pixel‐based and object‐based approaches typically employ a linear two‐staged workflow of segmentation and classification. Pixel‐based methods segment a classified image to address geo‐objects in image space. In contrast, object‐based approaches classify a segmented image to identify geo‐objects from raster datasets. These methods lack the ability to simultaneously integrate the geometry and theme of geo‐objects in image space. This article explores Geographical Vector Agents (GVAs) as an automated and intelligent processing unit to directly address real‐world objects in the process of remote sensing image classification. The GVA is a distinct type of geographic automata characterized by elastic geometry, dynamic internal structure, neighborhoods and their respective rules. We test this concept by modeling a set of objects on a subset IKONOS image and LiDAR DSM datasets without the setting parameters (e.g. scale, shape information), usually applied in conventional Geographic Object‐Based Image Analysis (GEOBIA) approaches. The results show that the GVA approach achieves more than 3.5% improvement for correctness, 2% improvement for quality, although no significant improvement for completeness to GEOBIA, thus demonstrating the competitive performance of GVAs classification. 相似文献
7.
Terrain analysis uses different workflows to extract features from terrain models for the purpose of understanding topographic patterns and processes. However, the results of different workflows often conflict, leading to uncertainties about feature locations. Instead of relying upon a single workflow, we suggest that a fusion of information from multiple workflows better informs terrain analysis. From terrain data with different degrees of variability, we extracted terrain features related to the set of topographic surface network feature classes {peaks, pits, saddles, ridges, courses} using workflows from free, open-source, and commercial software. A multi-scale analysis produced terrain features with fuzzy membership values for various feature classes and revealed that terrain locations can exhibit characteristics of all classes. Multi-feature maps were created by determining at each location the dominant and second-ranked features, and an uncertainty value. Our multi-method approach incorporated all of the workflows’ multi-scale results and again produced multi-feature maps that increased the confidence of some features and reduced the signal of dissimilar results. We also found that high variability terrain produced crisper features in both spatial extent and membership strength. Our overall conclusion is that multi-scale, multi-feature, and multi-method analyses clarify terrain feature uncertainty. 相似文献
8.
针对遥感影像面向对象分析技术存在的“分类过程中专家分析不同带来的分类结果不一致”问题,提出地理本体驱动的“地理实体描述-模型构建-影像对象分类”解译框架。首先,利用地理本体建立影像对象客观特征与地理专家知识的联系,实现对地理实体的描述与表达;其次,利用知识工程方法以及计算机可操作的形式化本体语言构建影像对象特征、分类器的本体模型,形成语义网络模型;最后,联合语义网络模型与专家规则实现影像对象的语义分类。地表覆盖分类实验结果表明,该方法不仅能够得到反映真实地理对象的遥感影像分类结果,而且能够掌握地理实体的语义信息,实现地表覆盖分类知识的共享与语义网络模型的复用,为遥感影像面向对象分析提供了一种全局性的解译分析框架及其方法。 相似文献
9.
ABSTRACTVirtual globes are technologies for visual navigation through a three-dimensional, multi-resolution model of the entire planet. Data representations used in virtual globes, however, lack geometric flexibility at high-resolution levels of the planet-wide terrain surface. This is a problem especially if boundaries between individual geospatial features and the terrain are important. A novel integration of individual polygonal boundaries with a specific multi-resolution representation of the planet-wide terrain is developed in this article. In the preparation stage, the integration relies on an original simplification algorithm applied to the polygonal boundaries between geospatial features and the terrain. Its output is a multiple level-of-detail (LOD) geometry, which can be combined with a known multi-LOD representation of the terrain that uses run-time triangulation. This data representation is suitable for storage in existing database systems, avoids any data redundancy across LODs, and is even independent of the subdivision schema that partitions the planet's surface for the sake of dealing with LODs. At run-time, a novel reconstruction algorithm stitches geometric parts from different LODs together in a manner that augments the multi-LOD representation of the terrain. Within a certain proximity range from a given position, the method reconstructs a scene that preserves topological relations between the boundaries of geospatial features with the terrain. The method also guarantees that certain nearest proximity to the given position consists of the best geometries that correspond to the original datasets. Such properties of the method close up the gap between a mere exploratory visualization of static, pre-generated models and the models supporting geospatial analysis, which is deemed crucial for applications in Geographic Information Systems, Building Information Modelling and other software industries. A prototype implementation and experiment results that prove this method are also presented. 相似文献
10.
面向地质建模的三维体元拓扑数据模型研究 总被引：27，自引：0，他引：27
在对地质对象的基本特征和计算机三维地质建模的基本要求进行讨论的基础上，提出了面向对象的三维体元拓扑数据模型。在该数据模型中，用面向对象的方法将地质对象抽象为点、线、面、体，体类又进一步划分为复合体、复杂体、简单体和体元四类。对所有对象类设计了12种拓扑关系和相应的数据结构。 相似文献
11.
Object based image analysis for remote sensing 总被引：3，自引：0，他引：3
T. Blaschke 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(1):2-16
Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes. 相似文献
12.
From fields to objects: A review of geographic boundary analysis 总被引：12，自引：0，他引：12
Geographic boundary analysis is a relatively new approach unfamiliar to many spatial analysts. It is best viewed as a technique
for defining objects – geographic boundaries – on spatial fields, and for evaluating the statistical significance of characteristics
of those boundary objects. This is accomplished using null spatial models representative of the spatial processes expected
in the absence of boundary-generating phenomena. Close ties to the object-field dialectic eminently suit boundary analysis
to GIS data. The majority of existing spatial methods are field-based in that they describe, estimate, or predict how attributes
(variables defining the field) vary through geographic space. Such methods are appropriate for field representations but not
object representations. As the object-field paradigm gains currency in geographic information science, appropriate techniques
for the statistical analysis of objects are required. The methods reviewed in this paper are a promising foundation. Geographic
boundary analysis is clearly a valuable addition to the spatial statistical toolbox.? This paper presents the philosophy of,
and motivations for geographic boundary analysis. It defines commonly used statistics for quantifying boundaries and their
characteristics, as well as simulation procedures for evaluating their significance. We review applications of these techniques,
with the objective of making this promising approach accessible to the GIS-spatial analysis community. We also describe the
implementation of these methods within geographic boundary analysis software: GEM.
Received: 22 March 1999 / Accepted: 7 September 1999 相似文献
13.
A Framework for Modeling Uncertainty in Spatial Databases 总被引：1，自引：0，他引：1
A Morris 《Transactions in GIS》2003,7(1):83-101
Geographic Information Systems and spatial databases are inherently suited for fuzziness, because of the uncertainty inherent in the assimilation, storage, and representation of spatial data. These objects may also have naturally occurring imprecise boundaries. It is difficult to store and represent these objects while continuing to demonstrate the uncertainty inherent in the objects. This paper describes a fuzzy object–oriented framework to model spatial objects with either precise or uncertain boundaries that will also provide for fuzzy querying of these objects. A prototype system, FOOSBALL, which implements this framework is also discussed. 相似文献
14.
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. 相似文献
15.
Elizabeth A. McCartney Kari J. Craun Erin Korris David A. Brostuen Laurence R. Moore 《制图学和地理信息科学》2015,42(4):54-57
Using crowdsourcing techniques, the US Geological Survey’s (USGS) Volunteered Geographic Information (VGI) project known as “The National Map Corps (TNMCorps)” encourages citizen scientists to collect and edit data about man-made structures in an effort to provide accurate and authoritative map data for the USGS National Geospatial Program’s web-based The National Map. VGI is not new to the USGS, but past efforts have been hampered by available technologies. Building on lessons learned, TNMCorps volunteers are successfully editing 10 different structure types in all 50 states as well as Puerto Rico and the US Virgin Islands. 相似文献
16.
Ismail Colkesen Taskin Kavzoglu 《Journal of the Indian Society of Remote Sensing》2018,46(8):1233-1242
With the increase in spatial resolution of recent sensors, object-based image analysis (OBIA) has gained importance for producing detailed land use maps. One of the main advantages of OBIA is that a variety of spectral, spatial and textural features can be extracted for the segmented image objects that are later utilized in classification. However, using a large number of features not only increases the required computational time, but also requires a large number of ground samples, which is unavailable in most cases. For these reasons, feature selection (FS) has become an important research topic for OBIA based classification studies. In this study, three filter-based FS algorithms namely, Chi square, information gain and ReliefF were applied to determine the most effective object features that ensure high separability among landscape features. For this purpose, importance degree (i.e. ranks) of 110 input object features were firstly estimated by the algorithms, and correlation-based merit function was then applied to determine optimum feature subset size. Multi-resolution segmentation algorithm was applied for segmenting a WorldView-2 image. Support vector machine, random forest and nearest neighbour classifiers were all utilized to classify segmented image objects using the selected object features. Results revealed that the FS algorithms were effective for selecting the most relevant features. Also, the classifiers produced the highest performances with 24 out of 110 features selected by the information gain (IG) algorithm. Particularly, the support vector machine classifier produced the highest overall accuracy (92.00%) with 24 selected features determined by the IG algorithm. A significant improvement of about 4% was achieved by applying FS procedures that was found statistically significant in terms of Wilcoxon signed-ranks test. 相似文献
17.
In human cognition, both visual features (i.e., spectrum, geometry and texture) and relational contexts (i.e. spatial relations) are used to interpret very-high-resolution (VHR) images. However, most existing classification methods only consider visual features, thus classification performances are susceptible to the confusion of visual features and the complexity of geographic objects in VHR images. On the contrary, relational contexts between geographic objects are some kinds of spatial knowledge, thus they can help to correct initial classification errors in a classification post-processing. This study presents the models for formalizing relational contexts, including relative relations (like alongness, betweeness, among, and surrounding), direction relation (azimuth) and their combination. The formalized relational contexts were further used to define locally contextual regions to identify those objects that should be reclassified in a post-classification process and to improve the results of an initial classification. The experimental results demonstrate that the relational contexts can significantly improve the accuracies of buildings, water, trees, roads, other surfaces and shadows. The relational contexts as well as their combinations can be regarded as a contribution to post-processing classification techniques in GEOBIA framework, and help to recognize image objects that cannot be distinguished in an initial classification. 相似文献
18.
Liqiang Zhang Zhizhong Kang Jonathan Li Ling Yang 《International Journal of Applied Earth Observation and Geoinformation》2010
This paper presents a Web-based three-dimensional Geographic Information System (3DGIS) for Wenchuan earthquake disaster assessment. With the help of information technology resources, geoscientists are in a position to learn more about the structure of the earthquake in efficient ways. Due to huge spatial datasets of Wenchuan, China and narrow network bandwidth, general-purpose applications are difficult to transmit and visualize these datasets on the network. The application aims to interactively represent and transfer large spatial objects of Wenchuan County, China, as well as for dynamically rendering them in networking environments. Level-of-detail (LOD) terrain models and vector maps are created, and the server–client architecture is presented. The application provides an effective way for powerful access and manipulation of large-scale Wenchuan datasets. 相似文献
19.
20.
三维城市模型的研究与实践(英文) 总被引：3，自引：0，他引：3
The way we interact with spatial data has been changed from 2D map to 3D Virtual Geographic Environment (VGE). Three-dimensional
representations of geographic information on a computer are known as VGE, and in particular 3D city models provide an efficient
way to integrate massive, heterogenous geospatial information and georeferenced information in urban areas. 3D city modeling
(3DCM) is an active research and practice topic in distinct application areas. This paper introduces different modeling paradigms
employed in 3D GIS, virtual environment, and AEC/FM. Up-to-date 3DCM technologies are evolving into a data integration and
collaborative approach to represent the full spatial coverage of a city, to model both aboveground and underground, outdoor
and indoor environments including man-made objects and natural features with 3D geometry, appearance, topology and semantics.
Supported by the National Natural Science Foundation of China ( No. 40871212, No. 40671158), the Leading Academic Discipline
Project of Shanghai Educational Committee( No.J50104). 相似文献