基于gpu的遥感高光谱图像数字资源库

J. S. Cedillo, A. Plaza
{"title":"基于gpu的遥感高光谱图像数字资源库","authors":"J. S. Cedillo, A. Plaza","doi":"10.1109/SYNASC.2013.68","DOIUrl":null,"url":null,"abstract":"Hyperspectral imaging is a new technique in remote sensing in which an imaging spectrometer collects hundred of images (at different wavelength channels) for the same area on the surface of Earth. Over the last years, hyperspectral image data sets have been collected from a great amount of locations over the world using a variety of instruments for Earth observation. Only a small amount of them are available for public use and they are spread among different storage locations and exhibit significant heterogeneity regarding the storage format. Therefore, the development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we describe the development of a shared digital repository for remotely sensed hyperspectral data, which allows uploading new hyperspectral data sets along with meta-data, ground-truth and analysis results (spectral information). Such repository is presented as a web service for providing the management of images through a web interface, and it is available online from http://www.hypercomp.es/repository. Most importantly, the developed system includes a spectral unmixing-based content based image retrieval (CBIR) functionality which allows searching for images from the database using spectrally pure components or endmembers in the scene. A full spectral unmixing chain is implemented for spectral information extraction, which allows filtering images using the similarity of the spectral signature and abundance of a given ground-truth. In order to accelerate the process of obtaining the spectral information for new entries in the system, we resort to an efficient implementations of spectral unmixing algorithms of graphics processing units (GPUs).","PeriodicalId":91954,"journal":{"name":"Proceedings. International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","volume":"6 1","pages":"473-480"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Digital Repository for Remotely Sensed Hyperspectral Imagery on GPUs\",\"authors\":\"J. S. Cedillo, A. Plaza\",\"doi\":\"10.1109/SYNASC.2013.68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hyperspectral imaging is a new technique in remote sensing in which an imaging spectrometer collects hundred of images (at different wavelength channels) for the same area on the surface of Earth. Over the last years, hyperspectral image data sets have been collected from a great amount of locations over the world using a variety of instruments for Earth observation. Only a small amount of them are available for public use and they are spread among different storage locations and exhibit significant heterogeneity regarding the storage format. Therefore, the development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we describe the development of a shared digital repository for remotely sensed hyperspectral data, which allows uploading new hyperspectral data sets along with meta-data, ground-truth and analysis results (spectral information). Such repository is presented as a web service for providing the management of images through a web interface, and it is available online from http://www.hypercomp.es/repository. Most importantly, the developed system includes a spectral unmixing-based content based image retrieval (CBIR) functionality which allows searching for images from the database using spectrally pure components or endmembers in the scene. A full spectral unmixing chain is implemented for spectral information extraction, which allows filtering images using the similarity of the spectral signature and abundance of a given ground-truth. In order to accelerate the process of obtaining the spectral information for new entries in the system, we resort to an efficient implementations of spectral unmixing algorithms of graphics processing units (GPUs).\",\"PeriodicalId\":91954,\"journal\":{\"name\":\"Proceedings. International Symposium on Symbolic and Numeric Algorithms for Scientific Computing\",\"volume\":\"6 1\",\"pages\":\"473-480\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Symposium on Symbolic and Numeric Algorithms for Scientific Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYNASC.2013.68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Symposium on Symbolic and Numeric Algorithms for Scientific Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2013.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

高光谱成像是一种新的遥感技术,利用成像光谱仪采集地球表面同一区域的数百张不同波长通道的图像。在过去几年中,利用各种地球观测仪器从世界各地大量地点收集了高光谱图像数据集。其中只有一小部分可供公众使用,它们分布在不同的存储位置,并且在存储格式方面表现出明显的异质性。因此,开发标准化的高光谱数据存储库是遥感界的一个高度期望的目标。在本文中,我们描述了一个共享的遥感高光谱数据数字存储库的开发,该存储库允许上传新的高光谱数据集以及元数据、地基真值和分析结果(光谱信息)。这种存储库以web服务的形式呈现,通过web界面提供图像管理,可以从http://www.hypercomp.es/repository在线获得。最重要的是,开发的系统包括基于光谱分解的基于内容的图像检索(CBIR)功能,该功能允许使用场景中的光谱纯成分或端元从数据库中搜索图像。全光谱解混链实现了光谱信息提取,它允许使用光谱特征的相似性和给定的基础真值的丰度过滤图像。为了加速获取系统中新条目的光谱信息,我们采用图形处理单元(gpu)的高效实现光谱分解算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Digital Repository for Remotely Sensed Hyperspectral Imagery on GPUs
Hyperspectral imaging is a new technique in remote sensing in which an imaging spectrometer collects hundred of images (at different wavelength channels) for the same area on the surface of Earth. Over the last years, hyperspectral image data sets have been collected from a great amount of locations over the world using a variety of instruments for Earth observation. Only a small amount of them are available for public use and they are spread among different storage locations and exhibit significant heterogeneity regarding the storage format. Therefore, the development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we describe the development of a shared digital repository for remotely sensed hyperspectral data, which allows uploading new hyperspectral data sets along with meta-data, ground-truth and analysis results (spectral information). Such repository is presented as a web service for providing the management of images through a web interface, and it is available online from http://www.hypercomp.es/repository. Most importantly, the developed system includes a spectral unmixing-based content based image retrieval (CBIR) functionality which allows searching for images from the database using spectrally pure components or endmembers in the scene. A full spectral unmixing chain is implemented for spectral information extraction, which allows filtering images using the similarity of the spectral signature and abundance of a given ground-truth. In order to accelerate the process of obtaining the spectral information for new entries in the system, we resort to an efficient implementations of spectral unmixing algorithms of graphics processing units (GPUs).
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信