像素级高光谱图像与激光雷达数据的融合方法

C. D. Abraham, J. Aravinth
{"title":"像素级高光谱图像与激光雷达数据的融合方法","authors":"C. D. Abraham, J. Aravinth","doi":"10.1109/WISPNET.2018.8538460","DOIUrl":null,"url":null,"abstract":"Hyperspectral image data and LIDAR data have found to be complimentary modailities in case of remotely sensed images, which can be fused if both are geo-referenced. Hyperspectral images provide the spectral response of each object in the area and can be used to identify the material composition of the image which can be used for the object classification. LIDAR data provides the elevation and geometrical information of the objects in the scene. Pixel-level fusion ensures no loss of information because there is no dimensionality reduction. This paper assesses the different methods of pixel fusion like wavelet transform, IHS transform and linear pixel fusion.","PeriodicalId":6858,"journal":{"name":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","volume":"1 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fusion Methods for Hyperspectral Image and LIDAR Data at Pixel-Level\",\"authors\":\"C. D. Abraham, J. Aravinth\",\"doi\":\"10.1109/WISPNET.2018.8538460\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hyperspectral image data and LIDAR data have found to be complimentary modailities in case of remotely sensed images, which can be fused if both are geo-referenced. Hyperspectral images provide the spectral response of each object in the area and can be used to identify the material composition of the image which can be used for the object classification. LIDAR data provides the elevation and geometrical information of the objects in the scene. Pixel-level fusion ensures no loss of information because there is no dimensionality reduction. This paper assesses the different methods of pixel fusion like wavelet transform, IHS transform and linear pixel fusion.\",\"PeriodicalId\":6858,\"journal\":{\"name\":\"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)\",\"volume\":\"1 1\",\"pages\":\"1-3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WISPNET.2018.8538460\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WISPNET.2018.8538460","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在遥感图像的情况下,高光谱图像数据和激光雷达数据是互补的,如果两者都是地理参考,则可以融合。高光谱图像提供了区域内每个物体的光谱响应,可以用来识别图像的物质组成,从而用于物体分类。激光雷达数据提供了场景中物体的高程和几何信息。像素级融合保证了信息的不丢失,因为没有降维。对小波变换、IHS变换和线性像素融合等不同的像素融合方法进行了研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Fusion Methods for Hyperspectral Image and LIDAR Data at Pixel-Level
Hyperspectral image data and LIDAR data have found to be complimentary modailities in case of remotely sensed images, which can be fused if both are geo-referenced. Hyperspectral images provide the spectral response of each object in the area and can be used to identify the material composition of the image which can be used for the object classification. LIDAR data provides the elevation and geometrical information of the objects in the scene. Pixel-level fusion ensures no loss of information because there is no dimensionality reduction. This paper assesses the different methods of pixel fusion like wavelet transform, IHS transform and linear pixel fusion.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Deep Reinforcement Learning for the Capacitated Vehicle Routing Problem with Soft Time Window Integrated Interference Solutions Between 5G and Satellite Systems Modulation Recognition Method of MAPSK Signal Artificial Intelligence Routing Method in Wireless Sensor Network for Sewage Treatment Monitoring Electromagnetically Induced Transparency in a Coupled NV Spin-Mechanical Resonator System
×
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