Emmanuel Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez
{"title":"用于遥感图像分类的全卷积神经网络","authors":"Emmanuel Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez","doi":"10.1109/igarss.2016.7730322","DOIUrl":null,"url":null,"abstract":"We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution, producing a final dense image labeling. Contrary to previous frameworks, our network contains only convolution and deconvolution operations. Experiments on aerial images show that our network produces more accurate classifications in lower computational time.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"140","resultStr":"{\"title\":\"Fully convolutional neural networks for remote sensing image classification\",\"authors\":\"Emmanuel Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez\",\"doi\":\"10.1109/igarss.2016.7730322\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution, producing a final dense image labeling. Contrary to previous frameworks, our network contains only convolution and deconvolution operations. Experiments on aerial images show that our network produces more accurate classifications in lower computational time.\",\"PeriodicalId\":179622,\"journal\":{\"name\":\"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"140\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/igarss.2016.7730322\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/igarss.2016.7730322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fully convolutional neural networks for remote sensing image classification
We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution, producing a final dense image labeling. Contrary to previous frameworks, our network contains only convolution and deconvolution operations. Experiments on aerial images show that our network produces more accurate classifications in lower computational time.