Sierra Hickman, Vishnu Anand Muruganandan, S. Weddell, R. Clare
{"title":"Image Metrics for Deconvolution of Satellites in Low Earth Orbit","authors":"Sierra Hickman, Vishnu Anand Muruganandan, S. Weddell, R. Clare","doi":"10.1109/IVCNZ51579.2020.9290535","DOIUrl":null,"url":null,"abstract":"Satellites and space debris clutter low Earth orbital paths, causing concern for future launches as the clutter increases the probability of in-orbit collisions. Therefore, it is important to track and characterise these objects. However, Earth’s atmosphere distorts images collected from ground-based telescopes, which can be reduced through post-processing deconvolution to improve images of satellites and space debris. A metric is needed to quantity the quality of the images and deconvolution of these extended objects at finite distances; as well as to characterise the structure and brightness for un-symmetrical satellites in low Earth orbit. This paper uses images of the International Space Station to investigate the use of the structural similarity metric and the regional properties as potential satellite imaging metrics. Our results show that the similarity metric can characterise the orientation of the satellite relative to the observer, while the regional properties serve to quantity the image quality and improvement due to deconvolution.","PeriodicalId":164317,"journal":{"name":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVCNZ51579.2020.9290535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Satellites and space debris clutter low Earth orbital paths, causing concern for future launches as the clutter increases the probability of in-orbit collisions. Therefore, it is important to track and characterise these objects. However, Earth’s atmosphere distorts images collected from ground-based telescopes, which can be reduced through post-processing deconvolution to improve images of satellites and space debris. A metric is needed to quantity the quality of the images and deconvolution of these extended objects at finite distances; as well as to characterise the structure and brightness for un-symmetrical satellites in low Earth orbit. This paper uses images of the International Space Station to investigate the use of the structural similarity metric and the regional properties as potential satellite imaging metrics. Our results show that the similarity metric can characterise the orientation of the satellite relative to the observer, while the regional properties serve to quantity the image quality and improvement due to deconvolution.