Cross-sensor transfer learning for fire smoke scene detection using variable-bands multi-spectral satellite imagery aided by spectral patterns

IF 3 3区 地球科学 Q2 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY International Journal of Remote Sensing Pub Date : 2024-05-07 DOI:10.1080/01431161.2024.2343430
Liang Zhao, Jixue Liu, Stefan Peters, Jiuyong Li, Norman Mueller, Simon Oliver
{"title":"Cross-sensor transfer learning for fire smoke scene detection using variable-bands multi-spectral satellite imagery aided by spectral patterns","authors":"Liang Zhao, Jixue Liu, Stefan Peters, Jiuyong Li, Norman Mueller, Simon Oliver","doi":"10.1080/01431161.2024.2343430","DOIUrl":null,"url":null,"abstract":"This paper addresses the challenge of training deep learning models for fire smoke scene detection from multi-sensor, multi-spectral satellite imagery, where spectral bands vary and training data i...","PeriodicalId":14369,"journal":{"name":"International Journal of Remote Sensing","volume":"103 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/01431161.2024.2343430","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

This paper addresses the challenge of training deep learning models for fire smoke scene detection from multi-sensor, multi-spectral satellite imagery, where spectral bands vary and training data i...
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用光谱模式辅助变波段多光谱卫星图像进行火灾烟雾场景探测的跨传感器迁移学习
本文探讨了从多传感器、多光谱卫星图像中训练深度学习模型进行火灾烟雾场景检测所面临的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
International Journal of Remote Sensing
International Journal of Remote Sensing 工程技术-成像科学与照相技术
CiteScore
7.00
自引率
5.90%
发文量
219
审稿时长
4.8 months
期刊介绍: The International Journal of Remote Sensing ( IJRS) is concerned with the theory, science and technology of remote sensing and novel applications of remotely sensed data. The journal’s focus includes remote sensing of the atmosphere, biosphere, cryosphere and the terrestrial earth, as well as human modifications to the earth system. Principal topics include: • Remotely sensed data collection, analysis, interpretation and display. • Surveying from space, air, water and ground platforms. • Imaging and related sensors. • Image processing. • Use of remotely sensed data. • Economic surveys and cost-benefit analyses. • Drones Section: Remote sensing with unmanned aerial systems (UASs, also known as unmanned aerial vehicles (UAVs), or drones).
期刊最新文献
Optimizing predictions of environmental variables and species distributions on tidal flats by combining Sentinel-2 images and their deep-learning features with OBIA. Assessment of yield loss due to fall armyworm in maize using high-resolution multispectral spaceborne remote sensing Structural graph learning method for hyperspectral band selection Dynamic region growing approach for leaf-wood separation of individual trees based on geometric features and growing patterns Feature extraction via 3-D homogeneous attribute decomposition for hyperspectral imagery classification
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1