{"title":"基于深度学习的无人机多光谱遥感影像植被分类","authors":"Jiaming Xue, Shanlin Sun, Haimeng Zhao, Wei Chen","doi":"10.1109/ICCECE58074.2023.10135502","DOIUrl":null,"url":null,"abstract":"With the aim of providing a reliable prediction model for vegetation detection and ground classification, a multispectral dataset was produced for semantic segmentation, which utilizes multispectral UAV images and is based on a combination of support vector machines and manual annotation. Also, a 3D-UNet model is proposed on which the dataset is trained and experiments show that the model has achieved 89.9 % prediction for the validation set.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vegetation Classification of UAV Multispectral Remote Sensing Images Based on Deep Learning\",\"authors\":\"Jiaming Xue, Shanlin Sun, Haimeng Zhao, Wei Chen\",\"doi\":\"10.1109/ICCECE58074.2023.10135502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the aim of providing a reliable prediction model for vegetation detection and ground classification, a multispectral dataset was produced for semantic segmentation, which utilizes multispectral UAV images and is based on a combination of support vector machines and manual annotation. Also, a 3D-UNet model is proposed on which the dataset is trained and experiments show that the model has achieved 89.9 % prediction for the validation set.\",\"PeriodicalId\":120030,\"journal\":{\"name\":\"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCECE58074.2023.10135502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCECE58074.2023.10135502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Vegetation Classification of UAV Multispectral Remote Sensing Images Based on Deep Learning
With the aim of providing a reliable prediction model for vegetation detection and ground classification, a multispectral dataset was produced for semantic segmentation, which utilizes multispectral UAV images and is based on a combination of support vector machines and manual annotation. Also, a 3D-UNet model is proposed on which the dataset is trained and experiments show that the model has achieved 89.9 % prediction for the validation set.
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