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%.

翻译：本文提出一种基于迁移学习的系统，用于检测多镜面卫星（如未来立方星设计和詹姆斯·韦伯空间望远镜）中的镜片错位问题。当镜片因空间碎片等环境因素发生错位时，图像会产生被称为"重影"的偏移复制畸变。为检测镜片是否错位，我们采用在灰度卫星图像分块快速傅里叶变换上预训练的大规模图像模型。多镜面设计可包含任意数量的镜片，本研究针对4、6、8镜片结构的模拟立方星进行测试。系统设计需满足两个检测需求：卫星是否存在错位镜片，以及错位镜片的数量。重影强度与错位镜片数量成正比，为此构建的强度分类模型尝试对N-1个错位等级进行分类。实验表明：二值分类模型在八类别体系中达到98.75%的准确率，强度分类模型达到98.05%的准确率。