# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 1 Introduction
The Materials and Methods section, together with the Results, will be about the design of an AI-based star and RSO segmentation algorithm capable of filtering the faintest objects from background in several signal-to-noise (SN) level scenarios (stray light noise, ghost noise, SEUs). In particular this algorithm aims to provide information about the brightest objects within the FOV in order to facilitate star identification and object detection and reduce the memory storage burden of a star tracker for successive operations. By brightest objects, we mean the ones that stand out most inside an image. Here, the formulation of the segmentation problem is similar to the creation of a saliency map. In the predicted mask, just the most salient objects will appear. This philosophy seems to be suitable to detect stars and objects in low SN environments as strong stray light noise that could affect the star sensor image. The algorithm design aims to have few modules to reduce the algorithm size, complexity and hardware implementation difficulties in order to provide a reliable star detection product. The machine learning (ML) world was investigated to take advantage of the neural networks (NNs) capability of learning specific tasks, performing well against unpredictable situations and reducing algorithm complexity for segmentation purposes; this could avoid the problem of algorithm re-calibration to successfully process images in different noise conditions. Moreover, the CNNs are considered for this kind of task. They have the advantage of processing the image directly without iterative steps and without a local approach that would be power- and time-consuming. All of these features are designed along with the capability of resolving objects in strong stray-light environments if a suitable dataset for training is available. Contributions of this work are as follows:  
* Creation and sharing of a balanced dataset of real and simulated star images for training, validation and testing of the CNN block. * Design of an AI-based brightest object detection algorithm capable of providing the most significant information for star identification and object tracking algorithms. * Comparison of our proposed AI-based algorithms with traditional segmentation algorithms. * Validation of the AI algorithm both with star tracker simulated images and real images. * Proposal of a an electro-optical payload for brightest objects segmentation in the onboard night sky images and a validation mission on a small platform. The work is organized as follows: a brief overview of the CNN and U-Net model is described in the Materials and Methods section, together with the proposed dataset and our proposed segmentation-clustering algorithm scheme. Then, a description of the traditional algorithms used for comparison will follow, along with a description of the considered performance indices. Results of U-Net's training, validation and testing are then reported and compared with traditional algorithms. They are followed by a Discussion section about the obtained results with a payload concept proposal and a possible validation mission. In the end, a Conclusion section summarizes the achievements and future goals.