# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 1 Introduction
Artificial intelligence could be an appealing and useful tool to face this challenging problems due to the recent development and application of AI in image processing. Star segmentation is a task that has many aspects in common with the segmentation of dim small targets, whose primary goal is to enhance the contrast between the target itself and the background, regardless of its nature; segmentation must be performed in every possible situation (noise, blurring, angular motion of the camera and/or motion of the target). All these issues also occur in star tracker images. Dim small target segmentation has been studied for both synthetic aperture radar (SAR) and optical images. [PERSON] et al. [11] proposed the application of a lightweight patch-to-pixel (P2P) CNN for ship detection in PolSAR (polarimetric SAR) images. Their approach prefers the use of dilated convolutional layers rather than conventional convolutional layers in order to expand the receptive field without adding any model parameter. [PERSON] and [PERSON] [12] employed a more general-purpose target segmentation using a CNN on infrared images, testing it on both real and synthetic images; however, their study suffers from the necessity for a large amount of training data to be fed to the network. [PERSON] et al. [13] overcame the issue of large training data by using the MNIST [14] database in such a way that it simulates images having similar properties to the long-range infrared images. [PERSON] [15] proposed an autonomous target recognition in forward looking infrared (FLIR) images based on different deep CNN architectures and thresholding. However, this approach suffers from a lack of reliability in a real-world scenario. [PERSON] and [PERSON] [16] based their studies on the use of a CNN and a denoising autoencoder network by treating the small targets as background noise and therefore transforming the segmentation task into a denoising task. The use of the famous biomedical-based U-Net for small target segmentation tasks has not been broadly investigated as of this writing, but [PERSON] et al. [17] proposed an enhanced symmetric attention (EAA) U-Net, which employs information extracted in the same layer to focus on the target and cross-layer information to learn higher level features. [PERSON] et al. [18; 19] investigated a more specialized version of the small target segmentation problem by going deeper into the problem of segmenting star images by proposing StarNet. This network is particularly complex due to the fact that its training is carried out in a multistage approach, an aspect that affects the training time in a negative way. In addition to this issue, StarNet is pre-trained on weights taken from the first three stages of VGG16 [20], whose initial input size must be a three-channel image. This forced choice translates into a computationally heavy process that affects the prediction time and could require top choice GPUs. All these aspects seem to make real-time image processing unfeasible. The U-Net proposed in this work tries to overcome this problem by working with mono-channel images by exploiting a simpler network architecture and training strategy. The present work is focused on an AI-based image segmentation module of a proposed star-tracker-based payload for attitude determination and extended RSO detection functions. This payload is thought to investigate and validate the AI behaviour for onboard optical sensor applications for small satellite missions. This choice can guarantee a high segmentation quality of star tracker images against a variety of noise scenarios without the need for intensive calibration activity. Indeed, it represents a robust processing solution for commercial and cheap star sensors which can be used in small platform missions. The Discussion section will be devoted to a description of the integrated hardware and software payload (design, optical head specifications, component choice and validation mission).