{"title":"天宫遥感自然场景智能识别与可解释性分析","authors":"Kunnan Liu, J. Li, Guofeng Xu, Peng Wang","doi":"10.1117/12.2671376","DOIUrl":null,"url":null,"abstract":"This paper focuses on the intelligent recognition of images in the Tiangong remote sensing image dataset and its interpretability analysis. In this paper, we classified the aforementioned dataset, retrained the Resnet-18 model on the training set, and then verified the results on the validation set with an accuracy of 97.9%. Furthermore, this paper presented an interpretability analysis of deep learning for intelligent recognition of the Tiangong remote sensing image dataset.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tiangong remote sensing natural scene intelligent recognition and interpretablity analysis\",\"authors\":\"Kunnan Liu, J. Li, Guofeng Xu, Peng Wang\",\"doi\":\"10.1117/12.2671376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper focuses on the intelligent recognition of images in the Tiangong remote sensing image dataset and its interpretability analysis. In this paper, we classified the aforementioned dataset, retrained the Resnet-18 model on the training set, and then verified the results on the validation set with an accuracy of 97.9%. Furthermore, this paper presented an interpretability analysis of deep learning for intelligent recognition of the Tiangong remote sensing image dataset.\",\"PeriodicalId\":120866,\"journal\":{\"name\":\"Artificial Intelligence and Big Data Forum\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence and Big Data Forum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2671376\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence and Big Data Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2671376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tiangong remote sensing natural scene intelligent recognition and interpretablity analysis
This paper focuses on the intelligent recognition of images in the Tiangong remote sensing image dataset and its interpretability analysis. In this paper, we classified the aforementioned dataset, retrained the Resnet-18 model on the training set, and then verified the results on the validation set with an accuracy of 97.9%. Furthermore, this paper presented an interpretability analysis of deep learning for intelligent recognition of the Tiangong remote sensing image dataset.
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