Fuzzy segmentation for object‐based image classification

Lizarazo, Ivan; Elsner, Paul

doi:10.1080/01431160802460062

Cited by 34 publications

(25 citation statements)

References 13 publications

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“…This particular setup of the defuzzification step leads to time consuming trial and error experiments which add significant effort to the whole classification process. Fuzzy sets concepts have been implemented in the segmentation stage of OBIA only very recently (Lizarazo & Elsner, 2009). This chapter links into this work and pursues the argument that a much more natural way for dealing with uncertainty is to apply fuzzy set concepts at the very beginning of the OBIA process, that is in the segmentation stage.…”

Section: Fuzzy Sets and Image Classificationmentioning

confidence: 99%

“…Lizarazo & Elsner (2009) applied the fuzzy segmentation method to classify the University image, a hyperspectral dataset of the University of Pavia (Italy) that was collected by the Hysens project on 8 th July 2002 (Gamba, 2004). The University data set size is 610x339 pixels.…”

Section: Application Of the Proposed Frameworkmentioning

confidence: 99%

“…For the final defuzzification stage, a support vector machine (SVM) was used to output the intended land cover classification. The overall classification accuracy reported by Lizarazo & Elsner (2009) The performance of the proposed fuzzy segmentation method was evaluated by using an earlier classification of the University data set (Aksoy, 2006) …”

Section: Application Of the Proposed Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Segmentation of Remotely Sensed Imagery: Moving from Sharp Objects to Fuzzy Regions

Lizarazo¹,

Elsner²

2011

Image Segmentation

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Section: Fuzzy Sets and Image Classificationmentioning

confidence: 99%

Section: Application Of the Proposed Frameworkmentioning

confidence: 99%

Section: Application Of the Proposed Frameworkmentioning

confidence: 99%

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Segmentation of Remotely Sensed Imagery: Moving from Sharp Objects to Fuzzy Regions

Lizarazo¹,

Elsner²

2011

Image Segmentation

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“…Details on the implementation of these models can be found in. 20,22,34 The feature analysis stage of the implemented case study was restricted to the calculation of ANDI indices for the following pairs of fuzzy image regions: meadows & trees, meadows & soil, asphalt & bitumen, asphalt & metal, and asphalt & gravel. This means that for the defuzzification stage, 14 predictor variables were available: 9 fuzzy image regions, plus 5 layers with ANDI indices.…”

mentioning

confidence: 99%

Fuzzy segmentation for geographic object-based image analysis

Lizarazo¹,

Elsner²

2009

SPIE Proceedings

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Image segmentation partitions remote sensing images into image objects before assigning them to categorical land cover classes. Current segmentation methods require users to invest considerable time and effort in the search for meaningful image objects. As an alternative method we propose 'fuzzy' segmentation that offers more flexibility in dealing with remote sensing uncertainty. In the proposed method, original bands are processed using regression techniques to output fuzzy image regions which express degrees of membership to target land cover classes. Contextual properties of fuzzy regions can be measured to indicate potential spectral confusion. A 'defuzzification' process is subsequently conducted to produce the categorical land cover classes. This method was tested using data sets of both high and medium spatial resolution. The results indicate that this approach is able to produce classification with satisfying accuracy and requires very little user interaction.

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“…In this project, no ancillary data are used. Another group of articles use fuzzy logic to deal with the mentioned complexity Fuzzy logic, which is developed by [11], has been used in image classification in several studies [12][13][14][15][16][17][18][19][20]. In [12], a fuzzy membership matrix for supervised image classification was used.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Satellite High Resolution Image Classification

Bharti¹,

Kurmi²

2017

IJCA

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The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process Image classification is the most important process in pattern reorganization. Image classification is one of the most complex areas in image processing. It is more complex and difficult to classify if it contain blurry and noisy content. There are several methods to classify images and they provide good classification result but they fail to provide satisfactory classification result when the image contains blurry and noisy content. The two main methods for image classification are supervised and unsupervised classification. In general, this is a final stage of pattern matching. The classification process described the percentage of accuracy in pattern recognition. Feature extraction is another vital stage in pattern matching. These extracted feature are used for classification of the image database, that is pattern matching.

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Fuzzy segmentation for object‐based image classification

Cited by 34 publications

References 13 publications

Segmentation of Remotely Sensed Imagery: Moving from Sharp Objects to Fuzzy Regions

Segmentation of Remotely Sensed Imagery: Moving from Sharp Objects to Fuzzy Regions

Fuzzy segmentation for geographic object-based image analysis

A Survey of Satellite High Resolution Image Classification

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