## 1 Introduction  
Thresholding segmentation [1] is an important process for space object recognition and extraction in star images. Stars and skylight backgrounds are separated by differences in grayscale, and segmentation thresholdings commonly used today are global thresholds [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13] and local thresholdings [14; 15; 16; 17; 18; 19]. A global thresholding is to specify a unified thresholding for all pixels of an image, such as the Otsu method [2; 3; 4; 5], maximum entropy method [6; 7; 8; 9; 10] and the minimum error thresholding method [11], which are only applicable to star images where the gray levels of objects and backgrounds are clearly distinguished. Local thresholding is selected according to features of different regions in star images, such as the Bernsen algorithm [14], Niblack algorithm [15; 16], and layered detection strategy in irregularly sized subregions [19], and the selection of sub-blocks has a great impact on the results of image segmentation.  
However, due to the influence of bright moonlight, thin clouds and vignetting effect of optical system, the local image backgrounds fluctuate greatly, and even many gray saturation regions appear. In this case, both types of thresholdings are hard to achieve good segmentation between complex backgrounds and dark objects. Gray thresholding segmentation ignores the imaging characteristics of space objects, whose energy distributions are presented as approximately symmetric Gaussian distributions and bright spots diffused to the surroundings [20; 21; 22; 23]. However, 2D morphology of the bright spots is not always significant. On the one hand, for a dark target, its gray level is close to the intensity of the image background, and the 2D Gaussian morphology may be drowned by fluctuating backgrounds [24; 25]. On the other hand, the objects and stars have a certain degree of elongation in the direction of relative motion within a few seconds of exposure time, and Gaussian features in the direction change. Therefore, we can only use 1D Gaussian morphology of object streaks in the vertical direction of relative motion. However, for a locally discontinuous dim object streak, its 1D morphology at every position is not consistent everywhere in complex backgrounds, and recognition effects are poor when point spread function (PSF) of the same amplitude is used to fit an object streak [26]. There are also some methods that apply deep learning to target detection [27; 28], but a large amount of image data needs to be trained in the object detection process.  
We aims at achieving star image segmentation by using the morphological characteristics of objects without relying on gray thresholding. Through the above analysis, it is known that 2D Gaussian function cannot accurately describe object streak morphology, and fitting a streak with 1D PSF with the same amplitude is not suitable for the poor background environment of star images. Accordingly, we introduce 1D Gaussian functions with different amplitudes for fitting each group of image data, utilizing the correlation between the image data and theoretical object models to respectively identify each group of data with Gaussian shapes in the streak area. In addition, we analyze the standard deviation and the ratio of mean square error and variance in each group of local image data, set reasonable thresholdings to separate objects from image background and remove false alarms. According to Gaussian morphology of objects and stars, our correlation segmentation method is independent of gray thresholding, which can flexibly use the morphological features of space objects, making the influence of complex backgrounds weaker and achieving better image segmentation.