# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 1 Introduction  
In the framework of on-board autonomous star sensors algorithms, star segmentation and accurate centroid estimation are critical problems to face. The number of actual detected stars and the relative centroids' estimation accuracy have a huge impact on the success of star-identification-based attitude determination routines [1, 2]. Taking into account that star sensor images generally contain several noise sources (stray light noise, single point noise, single events upsets (SEUs) and so on), good initial processing of image data is mandatory. In this way, it is possible to improve the percentage of success in the star identification process [3, 4] and the possibility of retrieving accurate attitude information from images. A good image segmentation process is also needed for RSO detection, where often the image noise and the weak nature of an RSO streak can lead to an incorrect detection of the object and loss of precious information for the orbit determination (OD) modules. The process of image segmentation is needed to highlight the useful image information which is geometrically encoded in the image pixels. Often, not all the pixels in an image are needed, and a process of selection and exclusion is mandatory to focus attention on just that image part. Image segmentation faces and solves this problem bringing as a result a binarized image which is often called Mask. Through the convolution of this Mask with the original image, it is possible to extract photometric and geometrical information about the target pixels. In this case, we will not be interested in the photometric information, but this will be discussed in the next sections. Regarding image segmentation, in the last decades several traditional approaches have been proposed, classified according to the element they take into account to discern foreground and background pixels: image histograms, detection of image gradients, detection of object edges and complexity analysis techniques [1]. Among them, commonly used methods are the following:  
* Iterative threshold method [5]: It performs an iterative optimization. An initial threshold is set, and then the algorithm improves the estimation at every step with a suitable improvement strategy. The strategy should be fast enough in convergence and should improve the quality of the segmented image at each step. * Local threshold methods: They are based on the selection of an initial threshold value plus a margin. For a generic examined pixel, its value is compared with a reference one. This reference value is updated continuously and takes into account the local energy value of the surrounding pixels in such a fashion that characterizes the method. A local threshold method is described in [6], and it is based on a moving streak average, while in [7], the approach uses rectangular areas and the local contrast level. * [PERSON]'s algorithm [8]: It performs a more refined approach where the intraclass variance of the foreground and background pixels is considered as a cost index and where the selected threshold comes from its maximization. * [PERSON]'s method [9]: It is a localized thresholding algorithm in which the threshold is varied across the image as a function of the local intensity average and standard deviation. Each reported traditional method presents limitations. Iterative threshold methods rely on all the energy levels in the global image, and these constitute their limit: a simple strong stray-light scenario can badly affect the predictions of these methods. Moreover, when the histogram of a star image is unimodal, the traditional iterative threshold method is time-consuming and cannot reach a suitable segmentation. A local threshold method would fail in the same way if the difference between the star signal and the local noise intensity is lower than the margin. The Otsu method, instead, can only provide satisfactory results for thresholding if the histogram is not unimodal or close to unimodal [10].