# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 5 Conclusions  
In conclusion, this work has shown several aspects:  
* The increment of initial filters in the U-Net increases the accuracy of the model predictions in terms of image segmentation quality and brightest objects detection.
* The BOSS algorithm is capable of achieving satisfying segmentation performance against different signal-to-noise scenarios. It does not need any re-calibration activity after its design in contrast to traditional LT segmentation algorithms.
* The comparison shows that the BOSS algorithm has segmentation performances which are comparable with respect to the optimized LT algorithms.
* The BOSS algorithm is able to properly remove the uniform stray light noise, most of the SEUs, weakest objects in an image and ghost noise and is able to provide a good product for star identification routines.
* A balanced CNN dataset for night sky images segmentation has been realized and provided.
* The algorithm works fine, both with a simulated star tracker and with real images of the night sky (both from ground and in orbit platforms).
* Once trained with images from specific star sensors, the algorithm does not need to be calibrated again.
* The simple structure in the algorithm makes it simple to analyze, to implement and to validate.
* Early versions of this algorithm proved to be suitable for RSO detection when working with real images [46].
* The optical head: cut off magnitude, FOV and working frequency were selected.
* The SoC/FPGA processing hardware was selected.
* An IOV mission for validating the BOSS algorithm against the AD and RSO detection purposes was proposed.  
As future goals, the development of a real payload for a future IOV mission will be pursued:  
* The algorithm will be implemented in the SoC/FPGA environment [50].
* We will design a buffer to store both RAW images and algorithm masks for their download and for on-ground checks of the algorithm behaviour in space.
* We will implement classical attitude determination routines and RSO detection modules [40; 48] to assess the feasibility of the BOSS algorithm for these purposes.  
Conceptualization, M.M. and F.C.; methodology, M.M. and I.A.; validation, M.M. and I.A.; data curation, M.M. and I.A.; writing--original draft preparation, M.M. and I.A.; writing--review and editing, M.M., I.A. and F.C.; supervision, I.A. and F.C. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Visibility conditions.  
**Funding:** This research received no external funding and is conducted within the main author's Ph.D. research activity in astronomy astrophysics and space sciences XXXVI cycle.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** U-Net Dataset link [31].  
**Acknowledgments:** The author and co-authors want to thank [PERSON] of _Italian Space Agency \"Centro di Geodesia Spaziale Giuseppe Colombo\"_ Observatory for having provided night sky image data both for building the dataset and testing the algorithms.  
**Conflicts of Interest:** The authors declare no conflict of interest.