Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis

He, Hong; Xing, Haihua; Hu, Dan; Yu, Xianchuan

doi:10.1007/s11430-017-9224-6

Cited by 17 publications

(7 citation statements)

References 38 publications

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“…denote the upper and lower bounds of the interval-valued membership degree of data i x belonging to the k-th cluster, the interval-valued membership degree is expressed as [ ( ), ( )] [25]. And the upper and lower bounds of interval-valued fuzzy membership degree are described as follows:…”

Section: B-2 Adaptive Interval Type-2 Fuzzy C-means Algorithmmentioning

confidence: 99%

“…He et al proposed the AIVIT2FCM algorithm based on interval-valued data to generate interval type-2 fuzzy sets. The type-2 fuzzy theory is introduced to further describe the high-order fuzzy uncertainty between classes and improves the classification accuracy [25].…”

mentioning

confidence: 99%

“…It not only reduces the dependence on fuzzifier, but also retains the uncertainty description of membership function, and it is applied to solve the segmentation problem of remote sensing image. In particular, a number of scholars [15] [17] [21] [25] has verified the feasibility of interval type-2 clustering with different distance measures in remote sensing image segmentation. Therefore, to reduce the dependence on two fuzzifiers in the classical interval type-2 fuzzy clustering, this paper introduces two interval-valued distances into interval type 2 fuzzy clustering, and proposes a novel single fuzzifier interval type-2 fuzzy clustering based on interval-valued data model.…”

mentioning

confidence: 99%

“…Step6. [40], FCM [27], interval-valued fuzzy c-means clustering algorithm (AIVFCM) [28], FLICM [24], IT2FCM [3], AIT2FCM [18], AIVIT2FCM [25], and the proposed algorithm (AIVIT2FLICM) are tested respectively, and after the actual segmentation test, the best fuzzifier [2,3]…”

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confidence: 99%

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A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

Guo

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In the process of land cover segmentation from remote sensing image, there are some uncertainties such as "significant difference in class density", "different objects with same spectrum" and "same object with different spectra". Existing fuzzy c-means clustering is not sufficient to describe the high-order fuzzy uncertainties and cannot achieve accurate segmentation. Type-2 fuzzy set is perfect for handling with inter-class multiple uncertainties, and clustering algorithm can suppress the noise of remote sensing image effectively by incorporating local information. Therefore, on the basis of integrating local information, this paper proposes a robust single fuzzifier interval type-2 fuzzy local C-means clustering based on adaptive interval-valued data for land cover segmentation. Firstly, interval-valued data modeling is performed for remote sensing data, and remote sensing features are represented as interval-valued vectors, and the robust interval-valued distance measure that can maximize the distance between interval-valued numbers is used to generate an interval type-2 fuzzy set through robust fuzzy clustering. Secondly, this paper adopts an efficient type reduction method to seek equivalent type-1 fuzzy set adaptively, and realizes the segmentation of land cover by the principle of maximum type-1 fuzzy membership. The test results of multi-spectral remote sensing images show that the segmentation performance of this proposed algorithm outperforms existing state of the art adaptive interval type-2 fuzzy clustering algorithms, and it is beneficial to the interpretation of remote sensing image. Index Terms-fuzzy C-means clustering; interval-valued data; interval type-2 fuzzy sets; fuzzy local information; land cover segmentation. I. INTRODUCTIONuzzy theory, as the basis of fuzzy analysis, is constantly updated and developed. The defects of type-1 fuzzy clustering algorithm in dealing with uncertainty are gradually exposed, and type-2 fuzzy clustering algorithm has certain potential advantage in dealing with high order uncertainty. Zadeh introduced type-2 fuzzy set (T2FS) as the extension of the concept of ordinary fuzzy set. In essence, it is a "fuzzy fuzzy set". In particular, type 2 fuzzy set is widely used to deal

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Section: B-2 Adaptive Interval Type-2 Fuzzy C-means Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

Guo

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Images from remote sensing are hyperspectral, high-space, and high-resolution. Because photographs include more information, they may be used in novel ways [ 2 , 3 ]. Land remote sensing data is big, complex, and often updated.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images

Mary¹,

Pachar

Srivastava

et al. 2022

Computational Intelligence and Neuroscience

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Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image. A trained depth belief network (DBN) processes and divides fusion images, while a Softmax classifier determines the land type. As tested, the proposed approach can categorise all types of land. Traditional methods of detecting distant sensing photographs have limitations that can be overcome by using convolutional neural networks (CNN). Traditional techniques are incapable of combining deep learning elements while doing badly in classification. After PCA decreases data dimensionality, deep learning is applied to generate effective features that employ deep learning after PCA has reduced the dimensionality of the data. Principal component analysis is commonly used because of its effectiveness in attaining linear dimension reduction. It may be used on its own or as a starting point for further study into various different dimensionality reduction approaches. Data can be altered by remapping onto a new set of orthogonal axes using a process known as projection-based principal component analysis. Following remote sensing of land resources, the pictures were classified using a support vector machine. Euroset satellite images are used to assess the suggested approach. Accuracy and kappa have both increased. It was accurate and within 95.83 % of the planned figures. The classification findings’ kappa value and reasoning time were 95.87 % and 128 milliseconds, respectively. Both the model’s performance and the classification effect are excellent.

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Robust Possibilistic Fuzzy Additive Partition Clustering Motivated by Deep Local Information

Wu,

2024

Circuits Syst Signal Process

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Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis

Cited by 17 publications

References 38 publications

A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

Deep Learning Model for the Image Fusion and Accurate Classification of Remote Sensing Images

Robust Possibilistic Fuzzy Additive Partition Clustering Motivated by Deep Local Information

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