Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

Laha, Arijit; Pal, Nikhil R.; Das, Jayanta Kumar

doi:10.1109/tgrs.2006.864391

Cited by 40 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The Dempster's combination rule was used to combine evidences provided by single rules. A work on combining a fuzzy rule-based system with the Dempster-Shafer theory was also reported recently by Laha et al (2006). In this work a single fuzzy rule generated a fuzzy label vector.…”

Section: Ensemble Systems and Dempster's Combination Rulementioning

confidence: 80%

Building ensemble classifiers using belief functions and OWA operators

Reformat

Yager

2007

Soft Comput

View full text Add to dashboard Cite

A pervasive task in many forms of human activity is classification. Recent interest in the classification process has focused on ensemble classifier systems. These types of systems are based on a paradigm of combining the outputs of a number of individual classifiers. In this paper we propose a new approach for obtaining the final output of ensemble classifiers. The method presented here uses the Dempster-Shafer concept of belief functions to represent the confidence in the outputs of the individual classifiers. The combing of the outputs of the individual classifiers is based on an aggregation process which can be seen as a fusion of the Dempster rule of combination with a generalized form of OWA operator. The use of the OWA operator provides an added degree of flexibility in expressing the way the aggregation of the individual classifiers is performed.

show abstract

Section: Ensemble Systems and Dempster's Combination Rulementioning

confidence: 80%

Building ensemble classifiers using belief functions and OWA operators

Reformat

Yager

2007

Soft Comput

View full text Add to dashboard Cite

show abstract

“…Furthermore, Lu et al [19] have confirmed the effectiveness and usefulness of fuzzy logic combined with an extension of DS theory in data fusion. In land cover classification, DS theory and fuzzy-contextual information [20] have been used in the classification of multispectral satellite images. This method reduces classification error compared to Markov random field (MRF)-based contextual classification.…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning

et al. 2017

View full text Add to dashboard Cite

Abstract:Andrews first proposed an equation to visualize the structures within data in 1972. Since then, this equation has been used for data transformation and visualization in a wide variety of fields. However, it has yet to be applied to satellite image data. The effect of unwanted, or impure, pixels occurring in these data varies with their distribution in the image; the effect is greater if impurity pixels are included in a classifier's training set. Andrews' curves enable the interpreter to select outlier or impurity data that can be grouped into a new category for classification. This study overcomes the above-mentioned problem and illustrates the novelty of applying Andrews' plots to satellite image data, and proposes a robust method for classifying the plots that combines Dempster-Shafer theory with fuzzy set theory. In addition, we present an example, obtained from real satellite images, to demonstrate the application of the proposed classification method. The accuracy and robustness of the proposed method are investigated for different training set sizes and crop types, and are compared with the results of two traditional classification methods. We find that outlier data are easily eliminated by examining Andrews' curves and that the proposed method significantly outperforms traditional methods when considering the classification accuracy.

show abstract

“…Laha et al [23], for instance, applied evidence theory to incorporate contextual information for further image classification using fuzzy rules. Ghimire et al [15] applied random forests, and Stuckens et al [39] employed a linkage-based clustering algorithm for land cover classification using contextual information.…”

Section: Introductionmentioning

confidence: 99%

Improving land cover classification through contextual-based optimum-path forest

Osaku

Nakamura²,

Pereira

et al. 2015

Information Sciences

View full text Add to dashboard Cite

a b s t r a c tTraditional machine learning algorithms very often assume statistically independent data samples. However, this is clearly not the case in remote sensing image applications, in which pixels present spatial and/or temporal dependencies. In this work, it has been presented an approach to improve land cover image classification using a contextual approach based on optimum-path forest (OPF) and the well-known Markov random fields (MRFs), hereinafter called OPF-MRF. In addition, it is also introduced a framework to the optimization of the amount of contextual information used by OPF-MRF. Experiments over high-and mediumresolution satellite (CBERS-2B, Landsat 5 TM, Ikonos-2 MS and Geoeye) and radar (ALOS-PALSAR) images covering the area of two Brazilian cities have shown the proposed approach can overcome several shortcomings related to standard OPF classification. In some cases, the proposed approach outperformed traditional OPF in about 9% of recognition rate, which is crucial for land cover classification.

show abstract

Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory

Cited by 40 publications

References 30 publications

Building ensemble classifiers using belief functions and OWA operators

Building ensemble classifiers using belief functions and OWA operators

A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning

Improving land cover classification through contextual-based optimum-path forest

Contact Info

Product

Resources

About