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

Abstract

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.

Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

“…It can combine the information of multiple coarse-grained sequences, reduce the standard deviation of entropy, and improve the stability of numerical results. The Fuzzy c-mean (FCM) clustering algorithm is an unsupervised method for data analysis and modeling, which is widely used in data classification and pattern recognition (Xu et al 2019;Zhang et al 2019). Through using the input features to generate the clustering center, calculating the Euclidean distance between the clustering points and the clustering centers, obtaining the membership degree of the clustering centers to divide the types of input features automatically.…”

SAR Image Segmentation Using Region Smoothing and Label Correction

“…It could simplify the display mode of the image and make it easy to understand and analyze [10,11]. There are four types of common SAR image segmentation methods: the morphological strategy [12][13][14][15], graph segmentation methods [16,17], clustering methods [1,18], and model-based methods [19,20]. In recent years, with the development of deep learning, there are more and more image segmentation methods realized by deep learning [21].…”

“…A distribution parameters to the level-set framework and had a good performance on SAR image segmentation. The MKJS-graph algorithm [19] used the over-segmentation algorithm to obtain the image superpixels, and the multi-core sparse representation model (MKSR) was used to conduct high-dimensional characterization of the superpixel features. The local spatial correlation and global similarity of the superpixels are used together to improve the segmentation accuracy.…”

Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures

“…Entropy-based indicators provide a different perspective in classical signal analysis. Entropy-based indicators have been effectively applied in several fields, such as biomedical engineering [34], economics [35], electronics [36], health monitoring [37], and even image and ground-penetrating radar signal processing [38,39]. Recent studies have shown the use of entropy-based indicators in vibration analysis [40][41][42][43][44][45][46][47], but mainly on general-purpose bearings at high speed.…”