Lawrence Egharevba, Sanjoy Kumar, N. Rishe, Hadi Amini, Malek Adjouadi
{"title":"“利用深度学习从卫星图像中检测和去除云影响区域”","authors":"Lawrence Egharevba, Sanjoy Kumar, N. Rishe, Hadi Amini, Malek Adjouadi","doi":"10.58245/ipsi.tir.2302.03","DOIUrl":null,"url":null,"abstract":"Deep Learning is becoming a very popular tool for generating and reconstructing images. Research has shown that deep learning algorithms can perform cutting-edge restoration tasks for various types of images. The performance of these algorithms can be achieved by training Deep Convolutional Neural Networks (DCNNs) with data from a large sample size. The processing of high-resolution satellite imagery becomes difficult when there are only a few images in a dataset. An approach based on the intrinsic properties of Deep Convolutional Neural Networks (DCNNs) is presented in this paper for the detection and removal of clouds from remote sensing images without any prior training. Our results demonstrated that the algorithm we used performed well when compared to trained algorithms.","PeriodicalId":41192,"journal":{"name":"IPSI BgD Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"\\\"Detecting and Removing Clouds Affected Regions from Satellite Images Using Deep Learning\\\"\",\"authors\":\"Lawrence Egharevba, Sanjoy Kumar, N. Rishe, Hadi Amini, Malek Adjouadi\",\"doi\":\"10.58245/ipsi.tir.2302.03\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep Learning is becoming a very popular tool for generating and reconstructing images. Research has shown that deep learning algorithms can perform cutting-edge restoration tasks for various types of images. The performance of these algorithms can be achieved by training Deep Convolutional Neural Networks (DCNNs) with data from a large sample size. The processing of high-resolution satellite imagery becomes difficult when there are only a few images in a dataset. An approach based on the intrinsic properties of Deep Convolutional Neural Networks (DCNNs) is presented in this paper for the detection and removal of clouds from remote sensing images without any prior training. Our results demonstrated that the algorithm we used performed well when compared to trained algorithms.\",\"PeriodicalId\":41192,\"journal\":{\"name\":\"IPSI BgD Transactions on Internet Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IPSI BgD Transactions on Internet Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.58245/ipsi.tir.2302.03\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IPSI BgD Transactions on Internet Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.58245/ipsi.tir.2302.03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
"Detecting and Removing Clouds Affected Regions from Satellite Images Using Deep Learning"
Deep Learning is becoming a very popular tool for generating and reconstructing images. Research has shown that deep learning algorithms can perform cutting-edge restoration tasks for various types of images. The performance of these algorithms can be achieved by training Deep Convolutional Neural Networks (DCNNs) with data from a large sample size. The processing of high-resolution satellite imagery becomes difficult when there are only a few images in a dataset. An approach based on the intrinsic properties of Deep Convolutional Neural Networks (DCNNs) is presented in this paper for the detection and removal of clouds from remote sensing images without any prior training. Our results demonstrated that the algorithm we used performed well when compared to trained algorithms.