{"title":"基于生成对抗网络的图像数据扩展及其在极光识别中的应用","authors":"Aoi Uchino, M. Matsumoto","doi":"10.1109/lgrs.2020.3012620","DOIUrl":null,"url":null,"abstract":"In recent years, automatic auroral image classification has been actively investigated. The baseline method has relied on supervised learning. As this approach requires a large amount of labeled teacher data, it is necessary to collect the data manually and label them, which is a time-consuming task. In this study, we proposed a method to extend an image data set by inputting training images into a deep convolutional generative adversarial network (DCGAN) and generating images in this manner. The proposed approach implied using both generated and original images to train the classifier. It could reduce the number of labeling operations performed manually. As an evaluation experiment, we performed classifier learning on the data sets before and after extension and confirmed that the classification accuracy was improved because of training on the data set after the extension.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1941-1945"},"PeriodicalIF":4.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3012620","citationCount":"2","resultStr":"{\"title\":\"Extension of Image Data Using Generative Adversarial Networks and Application to Identification of Aurora\",\"authors\":\"Aoi Uchino, M. Matsumoto\",\"doi\":\"10.1109/lgrs.2020.3012620\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, automatic auroral image classification has been actively investigated. The baseline method has relied on supervised learning. As this approach requires a large amount of labeled teacher data, it is necessary to collect the data manually and label them, which is a time-consuming task. In this study, we proposed a method to extend an image data set by inputting training images into a deep convolutional generative adversarial network (DCGAN) and generating images in this manner. The proposed approach implied using both generated and original images to train the classifier. It could reduce the number of labeling operations performed manually. As an evaluation experiment, we performed classifier learning on the data sets before and after extension and confirmed that the classification accuracy was improved because of training on the data set after the extension.\",\"PeriodicalId\":13046,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Letters\",\"volume\":\"18 1\",\"pages\":\"1941-1945\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/lgrs.2020.3012620\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/lgrs.2020.3012620\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/lgrs.2020.3012620","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Extension of Image Data Using Generative Adversarial Networks and Application to Identification of Aurora
In recent years, automatic auroral image classification has been actively investigated. The baseline method has relied on supervised learning. As this approach requires a large amount of labeled teacher data, it is necessary to collect the data manually and label them, which is a time-consuming task. In this study, we proposed a method to extend an image data set by inputting training images into a deep convolutional generative adversarial network (DCGAN) and generating images in this manner. The proposed approach implied using both generated and original images to train the classifier. It could reduce the number of labeling operations performed manually. As an evaluation experiment, we performed classifier learning on the data sets before and after extension and confirmed that the classification accuracy was improved because of training on the data set after the extension.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.