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Top1. Introduction
Image segmentation is an important stage in image processing, which has been widely used in the fields of image recognition, 3D reconstruction and target tracking (Hong et al. 2021; Teng et al. 2022). The areas of interest in the image are called the target or foreground, and the rest are called the background. The essence of image segmentation is to separate and extract the target area in the image (Hatamizadeh et al. 2022).
With the rapid development of remote sensing sensor technology, the spatial resolution of remote sensing image is constantly improved and the information contained is more abundant, so it is widely used in many fields (Tesfamikael et al. 2021). However, due to the improvement of resolution, the spectral heterogeneity of similar features is enhanced, which makes the processing of remote sensing images more difficult. Image segmentation is a key link in the process of image processing, and the results have an important impact on the subsequent work of image processing (Cheng et al. 2021; Minaee et al. 2021). Remote sensing image segmentation covers two basic tasks: 1) determine the number of ground objects (the number of pixels) contained in the region to be segmented in the remote sensing image, and 2) accurately segment the object regions.
In modern remote sensing image segmentation, due to the improvement of spatial resolution, various types of ground objects, complex background environment and difficulties in field investigation, it is difficult to manually interpret the number of land objects covered in remote sensing image (Xu et al. 2021; Liu et al. 2021). If the number of terrestrial species cannot be correctly pointed out, it will lead to wrong segmentation result. In addition, high resolution enhances the ability of remote sensing image to express the details of the covered surface. However, on the other hand, excessive detail expression will lead to the difference increase of the spectral measure of pixels in the similar ground objects (manifested as the increase of noise), which causes great difficulties for the segmentation (Lu 2021; Wang et al. 2022). Therefore, it has always been an important task in the research of high-resolution remote sensing image segmentation to automatically determine the class number and improve the anti-noise performance of the algorithm (Li et al. 2021).
In the many remote sensing image segmentation methods, the statistical segmentation framework is the most effective method. The segmentation method based on hybrid model fits the image to be segmented with the weighted sum of multiple probability distributions. The probability distribution of each mixed component describes the probability statistical distribution law of pixel spectral measure in a specific target class (Yuan et al. 2021). Among them, GMM (Gaussian mixture model) is widely used in remote sensing image segmentation because of its simple principle, intuitive structure and easy realization (Zhu et al. 2022; Chen et al. 2021). However, in the traditional GMM, only the gray information of the image is used, and the spatial relationship between the neighborhood pixels is not introduced, which leads to phenomenon that the segmentation result is very sensitive to the image noise.