Multispectral image segmentation by fuzzy clustering algorithm used Gaussian mixture model

Yu, 李玉 Li; Yan, 徐 艳 Xu; Xue-mei, 赵雪梅 Zhao; Quan-hua, 赵泉华 Zhao

doi:10.3788/ope.20172502.0509

Cited by 3 publications

(1 citation statement)

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“…As shown in Figure 9 In order to quantitatively evaluate the accuracy of the segmentation results, the template images in Figures 5a and 8a were used as the criterion to calculate the confusion matrix generated by the segmentation results (shown in Figures 6 and 9). The product accuracy, user accuracy, overall accuracy, and Kappa value were calculated according to the confusion matrix, as shown in Table 4 [52]. Indices with larger values correspond to higher accuracy segmentation, while accuracy often suffers from imbalanced data sets.…”

Section: Resultsmentioning

confidence: 99%

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Chen

et al. 2019

Remote Sensing

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Accurate multispectral image segmentation is essential in remote sensing research. Traditional fuzzy clustering algorithms used to segment multispectral images have several disadvantages, including: (1) they usually only consider the pixels’ grayscale information and ignore the interaction between pixels; and, (2) they are sensitive to noise and outliers. To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model. The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM). The Hidden Markov Random Field (HMRF) is introduced to define prior probability of neighborhood relationship, which is used as weights of the Gaussian components. The Lagrange multiplier method is used to solve the segmentation model. To evaluate the proposed segmentation algorithm, simulated and real multispectral images were segmented using the proposed algorithm and two other algorithms for comparison (i.e., Tsallis Fuzzy C-means (TFCM), Kullback–Leibler Gaussian Fuzzy C-means (KLG-FCM)). The study found that the modified algorithm can accelerate the convergence speed, reduce the effect of noise and outliers, and accurately segment simulated images with small gray level differences with an overall accuracy of more than 98.2%. Therefore, the algorithm can be used as a feasible and effective alternative in multispectral image segmentation, particularly for those with small color differences.

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Section: Resultsmentioning

confidence: 99%

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Chen

et al. 2019

Remote Sensing

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Simulation of Industrial Economic Development Model Based on Fuzzy Clustering Algorithm

Prakash,

Isaac. J,

Kumar

et al. 2023

2023 Second International Conference on Smart Technologies for Smart Nation (SmartTechCon)

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Water Detection in Urban Areas from GF-3

Liu

Shao

et al. 2018

Sensors

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The rapid and accurate detection of urban water is critical for urban management, river detection, and flood disaster assessment. This study is devoted to detecting water by GaoFen-3 (GF-3) Synthetic Aperture Radar (SAR) images with high spatial resolution. There have been no effective solutions that discriminate water and building shadows using a single SAR image in previous research. Inspired by the principle that every shadow has a corresponding building nearby, a new method is proposed in this study, whereby building shadows are removed depending on the correspondence of buildings and their shadows. The proposed method is demonstrated effective and efficient by experimental results on six GF-3 SAR images. The Receiver Operating Characteristic (ROC) curves of the water detection results indicate that the proposed method increases the Probability of Detection (PD) to 98.36% and decreases the Probability of False Alarm (PFA) to 1.91% compared with the thresholding method, where, at the same PFA level, the maximum PD of the thresholding method is 72.62% in all testing samples. The proposed method is capable of removing building shadows and detecting water with high precision in urban areas, which presents the great potential of high-spatial-resolution GF-3 images in terms of water resource management.

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Multispectral image segmentation by fuzzy clustering algorithm used Gaussian mixture model

Cited by 3 publications

References 0 publications

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Simulation of Industrial Economic Development Model Based on Fuzzy Clustering Algorithm

Water Detection in Urban Areas from GF-3

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