Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

Jia, Heming; Peng, Xiaoxu; Song, Wenlong; Oliva, Diego; Lang, Chunbo; Li, Yao

doi:10.3390/rs11080942

Cited by 23 publications

(6 citation statements)

References 65 publications

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“…In this section, the findings from satellite images after the implemented segmentation process are discussed. Besides the proposed technique in this paper, Watershed algorithm, K-means clustering algorithm, and TLMVO-Masi method [54] are selected as comparisons segmentation techniques. Taking Image 8 and Image 10 as examples, it is necessary to describe two images.…”

Section: G Results and Findings Of Imagesmentioning

confidence: 99%

“…In this paper, the three channel components of R, G, and B are firstly extracted. Next, the Masi entropy is calculated for each channel, and the objective function is maximized to obtain the optimal thresholds of the corresponding channel [54]. The RGB channel components are segmented by optimal thresholds and merged to form the final segmented image.…”

Section: B Masi Entropymentioning

confidence: 99%

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Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color Satellite Image Segmentation Using Masi Entropy

et al. 2019

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In order to realize the multilevel thresholding segmentation of color satellite images, a multistrategy emperor penguin optimizer (called MSEPO) is proposed to find the optimal threshold values for three channels of RGB images. Masi entropy is utilized as the objective function. Meanwhile, three strategies are introduced, namely highly disruptive polynomial mutation, Levy flight, and thermal exchange operator. Through these, the MSEPO is able to properly balance the exploration and exploitation mechanisms. Moreover, the convergence, accuracy and stability performance have been significantly enhanced. Tests are carried out on color Berkeley images and color satellite images at various threshold levels. The experimental results show that the proposed method achieves higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), higher Feature Similarity Index (FSIM), and shorter CPU time than seven state-ofthe-art optimization techniques. To present in a comprehensive manner, the computational complexity has also been analyzed in terms of time and space complexity. Wilcoxon rank sum test and Friedman test are also applied to statistical analysis. To sum up, MSEPO algorithm has achieved significant improvement and superior performance. What's more, the proposed technique is more suitable for high-dimensional segmentation of complex satellite images.INDEX TERMS Multilevel thresholding, satellite image segmentation, Masi entropy, emperor penguin optimizer, thermal exchange operator, multi-strategy.

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Section: G Results and Findings Of Imagesmentioning

confidence: 99%

Section: B Masi Entropymentioning

confidence: 99%

Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color Satellite Image Segmentation Using Masi Entropy

et al. 2019

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“…The chi-square χ 2 value and p-value are presented in Table 10. According to the chi-square distribution table, the critical value for 7 (8 algorithms − 1) degrees of freedom with 5% significant level is 14.067 [67,68]. As shown in Table 10, the chi-square values obtained at all threshold levels were much larger than the critical value, and the p-values acquired for all number of thresholds were far less than 0.05.…”

Section: Statistical Testsmentioning

confidence: 94%

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

et al. 2019

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An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

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“…In this paper, three channel components of R, G and B are extracted at first. Then, each channel is calculated by Masi entropy, and the objective function is maximized to find the optimal threshold for the corresponding channel [67]. The RGB channel components are divided by the optimal threshold and then merged to form the ultimate segmented image.…”

Section: B Masi Entropymentioning

confidence: 99%

A Logistic Chaotic Barnacles Mating Optimizer With Masi Entropy for Color Image Multilevel Thresholding Segmentation

Zheng

Sun

et al. 2020

IEEE Access

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Barnacles mating optimizer (BMO) is an evolutionary algorithm that simulates the mating and reproductive behavior of barnacle population. In this paper, an improved Barnacles mating optimizer based on logistic model and chaotic map (LCBMO) was proposed to produce the high-quality optimal result. Firstly, the logistic model is introduced into the native BMO to realize the automatic conversion parameters. This strategy maintains a proper relationship between exploitation and exploration. Then, the chaotic map is integrated to enhance the exploitation capability of the algorithm. After that, six variants based on LCBMO are compared to find the best algorithm on benchmark functions. Moreover, to the knowledge of the authors, there is no previous study on this algorithm for multilevel color image segmentation. LCBMO takes Masi entropy as the objective function to find the optimal threshold. By comparing different thresholds, different types of images, different optimization algorithms, and different objective functions, our proposed technique is reliable and promising in solving color image multilevel thresholding segmentation. Wilcoxon rank-sum test and Friedman test also prove that the simulation results are statistically significant. INDEX TERMS Barnacles mating optimizer, logistic model, chaotic map, masi entropy, multilevel thresholding, color image segmentation.

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Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

Cited by 23 publications

References 65 publications

Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color Satellite Image Segmentation Using Masi Entropy

Multi-Strategy Emperor Penguin Optimizer for RGB Histogram-Based Color Satellite Image Segmentation Using Masi Entropy

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

A Logistic Chaotic Barnacles Mating Optimizer With Masi Entropy for Color Image Multilevel Thresholding Segmentation

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