Image-based Detection of Segment Misalignment in Multi-mirror Satellites using Transfer Learning

TL;DR

This paper presents a transfer learning-based image analysis system to detect and quantify segment misalignment in multi-mirror satellites, demonstrating high accuracy on simulated CubeSat models with varying mirror counts.

Contribution

It introduces a novel transfer learning approach using FFT-based image features for detecting mirror misalignments in multi-mirror satellites, applicable to arbitrary mirror configurations.

Findings

Achieved 98.75% accuracy in classifying misaligned segments.

Successfully used FFT-based models for intensity classification of ghost images.

Demonstrated system effectiveness on simulated CubeSat models with 4, 6, and 8 segments.

Abstract

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%.