A probability-based multi-measure feature matching method in map conflation

Tong, Xiaohua; Shi, Wenzhong; Deng, Shengyuan

doi:10.1080/01431160903130986

Cited by 50 publications

(22 citation statements)

References 14 publications

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“…The third type is similar to the probability-based linear feature matching, i.e. only the matching measures are related to the polygon features, such as area and the distance between the polygons (Samal et al 2004;Deng et al 2007;Tong et al 2009;Kim et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Wang

Deng

Zhao

et al. 2015

Transactions in GIS

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Spatial data infrastructures, which are characterized by multi‐represented datasets, are prevalent throughout the world. The multi‐represented datasets contain different representations for identical real‐world entities. Therefore, update propagation is useful and required for maintaining multi‐represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real‐world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi‐represented feature matching based on spatial similarity and a back‐propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one‐to‐one matching but also to one‐to‐many and many‐to‐many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN‐based feature matching approach.

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Section: Introductionmentioning

confidence: 99%

“…However, the threshold has an important impact on the final matched results and there is only a little flexibility in this method. Hence, many methods are based on probability (Samal et al 2004;Deng et al 2007;Tong et al 2009;Kim et al 2010). The weight of each measure is determined subjectively based on its importance.…”

Section: Introductionmentioning

confidence: 99%

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Wang

Deng

Zhao

et al. 2015

Transactions in GIS

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“…Papers also develop important topics such as issues connected with uncertainty and error propagation Chun 2009, Stein andHamm 2009), scale transform in remote sensing products (Tao et al 2009), scale effects on albedo estimation in regions of mountainous terrain (Wen et al 2009) and validation issues in models (Huang et al 2009). Finally, papers also address issues associated with data conflation , Tong et al 2009), location uncertainty ) and how uncertainty information may be used to enhance analyses (Goncalves et al 2009) as well as discussions on the accuracy with which vegetation structural properties may be estimated ). The various topics addressed by the papers in this special issue reflect just a small part of the subject which will be further developed and explored at the next symposium, scheduled for Leicester, UK, in 2010.…”

mentioning

confidence: 98%

Preface: Spatial accuracy in remote sensing

Zhang¹,

Foody²

2009

International Journal of Remote Sensing

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“…In the field of geographic information, there are several studies regarding the classification of these measures, among them Tong et al [17], who defined three different categories:…”

Section: Inter-elements Matching (Inter-polygons)mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

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A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

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A probability-based multi-measure feature matching method in map conflation

Cited by 50 publications

References 14 publications

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Preface: Spatial accuracy in remote sensing

A Polygon and Point-Based Approach to Matching Geospatial Features

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