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.
Highlights
Image segmentation, which is a remote sensing technique dividing complex images into several continuous homogeneous regions, is fundamental in object-based image analysis [1,2,3,4]
We developed the fuzzy clustering algorithm combined with the Tsallis entropy and the Gaussian mixture model (TGMM-Fuzzy C-means (FCM)) as an alternative approach to image segmentation
This paper proposes the Fuzzy Tsallis Entropy-Clustering Algorithm Combined with Gaussian Mixture Model (TGMM-FCM)
Summary
Image segmentation, which is a remote sensing technique dividing complex images into several continuous homogeneous regions, is fundamental in object-based image analysis [1,2,3,4]. The FCM proposed by Bezdek has become ubiquitous in image segmentation, the approach continues to have major significant disadvantages, including: having no theoretical basis on weighting exponent m selection, neglecting image pixel grayscale correlation, sensitivity to noise and outliers, and inability to fit multi-peak characteristics of a remote sensing image. Aside from refining the objective function, a number of researchers have examined the dissimilarity measure based on the distance function and probability distribution model to improve further the accuracy of the fuzzy clustering algorithm. The traditional fuzzy clustering segmentation algorithm based on the Euclidean measure is sensitive to noise and outliers, and the segmentation results are affected by the shape, density, and size of clusters (i.e., pixel distribution in feature space and the number of pixels in homogeneous regions).
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