  In this paper, we introduce a system based on transfer learning for detecting
segment misalignment in multimirror satellites, such as future CubeSat designs
and the James Webb Space Telescope (JWST), using image-based methods. When a
mirror segment becomes misaligned due to various environmental factors, such as
space debris, the images can become distorted with a shifted copy of itself
called a "ghost image". To detect whether segments are misaligned, we use
pre-trained, large-scale image models trained on the Fast Fourier Transform
(FFT) of patches of satellite images in grayscale. Multi-mirror designs can use
any arbitrary number of mirrors. For our purposes, the tests were performed on
simulated CubeSats with 4, 6, and 8 segments. For system design, we took this
into account when we want to know when a satellite has a misaligned segment and
how many segments are misaligned. The intensity of the ghost image is directly
proportional to the number of segments misaligned. Models trained for intensity
classification attempted to classify N-1 segments. Across eight classes, binary
models were able to achieve a classification accuracy of 98.75%, and models for
intensity classification were able to achieve an accuracy of 98.05%.
