{"title":"基于神经网络技术的光学和雷达图像空间碎片要素识别方法","authors":"O.A. Mishukov, A.N. Smirnov, A.E. Zhitikhin","doi":"10.30898/1684-1719.2024.1.3","DOIUrl":null,"url":null,"abstract":"An improved Bayesian method is presented for recognising space debris elements from optical and radar images and is intended to identify potentially hazardous space debris elements for operational spacecraft. A set of informative features for recognising space debris elements is proposed. A modified Bayesian classifier based on a deep neural network with a sequential decision-making procedure is considered as a decisive rule. The quality requirements for the used optical and radar images are defined. The results of recognition probability estimation for different types of space debris elements with different number of used optical and radar images are obtained.","PeriodicalId":391686,"journal":{"name":"Journal of Radio Electronics","volume":"329 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Method of space debris element recognition from optical and radar images based on neural network technologies\",\"authors\":\"O.A. Mishukov, A.N. Smirnov, A.E. Zhitikhin\",\"doi\":\"10.30898/1684-1719.2024.1.3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improved Bayesian method is presented for recognising space debris elements from optical and radar images and is intended to identify potentially hazardous space debris elements for operational spacecraft. A set of informative features for recognising space debris elements is proposed. A modified Bayesian classifier based on a deep neural network with a sequential decision-making procedure is considered as a decisive rule. The quality requirements for the used optical and radar images are defined. The results of recognition probability estimation for different types of space debris elements with different number of used optical and radar images are obtained.\",\"PeriodicalId\":391686,\"journal\":{\"name\":\"Journal of Radio Electronics\",\"volume\":\"329 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Radio Electronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30898/1684-1719.2024.1.3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radio Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30898/1684-1719.2024.1.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Method of space debris element recognition from optical and radar images based on neural network technologies
An improved Bayesian method is presented for recognising space debris elements from optical and radar images and is intended to identify potentially hazardous space debris elements for operational spacecraft. A set of informative features for recognising space debris elements is proposed. A modified Bayesian classifier based on a deep neural network with a sequential decision-making procedure is considered as a decisive rule. The quality requirements for the used optical and radar images are defined. The results of recognition probability estimation for different types of space debris elements with different number of used optical and radar images are obtained.