Fuzzy Regions: Theory and Applications

Verstraete, Jörg; Hallez, Axel; Tré, Guy De

doi:10.1007/978-1-4020-6438-8_1

Cited by 9 publications

(10 citation statements)

References 5 publications

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“…Thus, a membership grade 1 indicates it belongs fully to the region. A membership grade 0 indicates that a pixel does not belong to the region (Verstraete et al, 2007). Figures 1 and 2 illustrate the main difference between hard segmentation which produces one single image at every scale, and fuzzy segmentation which produces a set of as many images as target classes exist.…”

Section: Fuzzy Image-regionsmentioning

confidence: 99%

“…Well known generic fuzzy operators include: union (UNI), intersection (INT), complement (COM), difference (DIF), and the principles of middle included (MID) and contradiction (CONT), and a number of specific operators for defuzzification, for instance alpha cut (ALP) and intensification (INS) (Ross, 2004). Geometric operations (surface area, distance to a fuzzy region) and specific geographic operations (minimum bounding rectangle, convex hull) are much more complex as a recent implementation has demonstrated (Verstraete et al, 2007).…”

Section: Feature Analysismentioning

confidence: 99%

“…Although the concept of fuzzy regions or objects has been used in geographic information science for some time, practical implementation of specific methods suited to the spatial realm has only recently started being developed (Verstraete et al, 2007). Well known generic fuzzy operators include: union (UNI), intersection (INT), complement (COM), difference (DIF), and the principles of middle included (MID) and contradiction (CONT), and a number of specific operators for defuzzification, for instance alpha cut (ALP) and intensification (INS) (Ross, 2004).…”

Section: Feature Analysismentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Image Segmentation for Urban Land-Cover Classification

Lizarazo¹,

Barros²

2010

photogramm eng remote sensing

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ABSTRACT:In this paper a general fuzzy approach for segmentation-based classification is proposed. Traditional segmentation techniques focus on partitioning imagery into image-objects with well-defined boundaries. Instead, the proposed methodology aims to produce and analyze fuzzy image-regions expressing degrees of membership to different target classes. This approach, called Fuzzy Image-Regions Method (FIRME), is suitable to deal with the spectrally and spatially complexity of urban landscapes. The main stages of the FIRME approach are described, including techniques to produce such regions, alternatives to measure region attributes, and a number of methods for defuzzification. The FIRME method is tested for an urban classification experiment using multi-spectral imagery from Bogota, Colombia. Results suggest that, in complex environments, the FIRME method may be a suitable alternative to hard segmentation as it performs well in discriminating between spectrally mixed geographic objects.

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Section: Fuzzy Image-regionsmentioning

confidence: 99%

Section: Feature Analysismentioning

confidence: 99%

Section: Feature Analysismentioning

confidence: 99%

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Fuzzy Image Segmentation for Urban Land-Cover Classification

Lizarazo¹,

Barros²

2010

photogramm eng remote sensing

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“…Since their formal introduction by Zadeh (Zadeh, 1965), fuzzy sets and fuzzy logic have been used in many fields of science including geoinformatics. It is not the aim of this paper to introduce fuzzy sets and logic theory (for more information on this topic, see Bělohlávek, 2002;Klir & Yuan, 1996;Novák, 1989;Verstaete et al, 2007), instead our goal is to produce a map using a fuzzy inference system (FIS) in an antecedent analysis.…”

Section: Introductionmentioning

confidence: 99%

Using a fuzzy inference system to delimit rural and urban municipalities in the Czech republic in 2010

et al. 2014

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Due to the suburbanisation process, it is becoming more difficult to properly define rural and urban areas in the Czech Republic. This delimitation problem has been intensively studied in Europe, including the Czech Republic, for decades, but only so-called 'crisp' rules have been set for the categorisation of urban and rural. This is no longer satisfactory because of substantial population movements. Our research focuses on applying fuzzy set theory to the delimitation of rural and urban areas and on the subsequent advanced cartographic visualisation. We used the principles of fuzzy regulation, or fuzzy inference systems, on socio-economic data to show the transitional character of municipalities. The generated Main map is at scale of 1:500,000, whereas secondary maps are at scale of 1:2,500,000. Map visualisation of municipalities in the Czech Republic provides a very unique combination of geographical information science, cartography and modern geo-computational methods. Information perception via a map is an adequate way to analyse geographic information, and the problem of delimiting rural and urban areas can be suitably visualised using these methods.

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“…Fuzzy Set Theory is also used to represent objects with vague shape (called fuzzy spatial objects in this category of models) (Robinson and Thongs 1986;Altman 1987;Burrough 1989;Zhan 1997;Schneider 2001;Bordogna and Chiesa 2003;Tang et al 2003;Tang 2004;Li and Li 2004;Dilo 2006;Verstraete et al 2007). Zhan (Zhan 1997) and Dilo (Dilo 2006) interpret a fuzzy spatial object as a fuzzy subset.…”

mentioning

confidence: 99%

Qualified topological relations between spatial objects with possible vague shape

Bejaoui

Pinet²,

Bédard

et al. 2009

International Journal of Geographical Information Science

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Broad boundary is generally used to replace one-dimensional boundary for spatial objects with vague shape. For regions with broad boundary, this concept should respect both connectedness and closeness conditions. Therefore, some real configurations, like regions with partially broad boundary (e.g. lake with rocky and swamp banks), are considered invalid. This paper aims to represent different levels of shape vagueness and consider them during the identification of topological relations. Then, an object with vague shape is composed by two crisp components: a minimal extent and a maximal extent. Topological relations are identified by applying the 9-Intersection model for the subrelations between the minimal and maximal extents of objects involved. Four subrelations are then represented through a 4-Intersection matrix used to classify the topological relations. For regions with broad boundary, 242 relations are distinguished and classified into 40 clusters. This approach supports an adverbial expression of integrity constraints and spatial queries

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Fuzzy Regions: Theory and Applications

Cited by 9 publications

References 5 publications

Fuzzy Image Segmentation for Urban Land-Cover Classification

Fuzzy Image Segmentation for Urban Land-Cover Classification

Using a fuzzy inference system to delimit rural and urban municipalities in the Czech republic in 2010

Qualified topological relations between spatial objects with possible vague shape

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