Geodata from Earth HSI bands as a result of direct implementation of Complex wavelet filter Bank for reduction

S. Swamy, S. Asutkar, G. Asutkar
{"title":"Geodata from Earth HSI bands as a result of direct implementation of Complex wavelet filter Bank for reduction","authors":"S. Swamy, S. Asutkar, G. Asutkar","doi":"10.1109/ICIICT1.2019.8741409","DOIUrl":null,"url":null,"abstract":"This paper unfolds the distinct feature of complex wavelets to dimension reduction of hyperspectral images. The algorithm of complex wavelet filter bank is implemented on Hyperspectral image bands to reduce the redundant information in terms of bands. In the recent literature on similar concept, the idea is implemented on spectral signatures of each band or in a single layer. The paper encompasses the complex wavelet filter bank advantages related to shift invariance and directionality properties on the hyperspectral image as a whole. The hyperspectral images carry volume of data and the image analysis needs the dimension reduction for the proper computation and detection. The unwanted bands can be ignored through the proposed algorithm. The reduction algorithm realizes the correlation between the bands of 64 band tiff image taken from ISRO database, India. The bands reduced to five in the proposed method efficient than existing real wavelet method.","PeriodicalId":118897,"journal":{"name":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIICT1.2019.8741409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper unfolds the distinct feature of complex wavelets to dimension reduction of hyperspectral images. The algorithm of complex wavelet filter bank is implemented on Hyperspectral image bands to reduce the redundant information in terms of bands. In the recent literature on similar concept, the idea is implemented on spectral signatures of each band or in a single layer. The paper encompasses the complex wavelet filter bank advantages related to shift invariance and directionality properties on the hyperspectral image as a whole. The hyperspectral images carry volume of data and the image analysis needs the dimension reduction for the proper computation and detection. The unwanted bands can be ignored through the proposed algorithm. The reduction algorithm realizes the correlation between the bands of 64 band tiff image taken from ISRO database, India. The bands reduced to five in the proposed method efficient than existing real wavelet method.
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
由于直接实现了复小波滤波器组对地球HSI波段的大地数据进行了约简
本文揭示了复杂小波在高光谱图像降维中的独特特点。在高光谱图像的波段上实现了复小波滤波器组算法,以减少波段上的冗余信息。在最近关于类似概念的文献中,该思想是在每个波段或单层的光谱特征上实现的。本文将复小波滤波器组在高光谱图像上的移位不变性和方向性等方面的优势作为一个整体加以论述。高光谱图像承载着大量的数据,为了进行正确的计算和检测,需要对图像进行降维处理。通过该算法可以忽略不需要的频带。该算法实现了印度ISRO数据库中64波段tiff图像的波段间相关。与现有的实小波方法相比,所提出的方法能有效地将波段缩减到5个。
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
自引率
0.00%
发文量
0
期刊最新文献
Design Of A Monitoring System For Waste Management Using IoT Survey on Private Blockchain Consensus Algorithms Object Recognition and Classification Based on Improved Bag of Features using SURF AND MSER Local Feature Extraction Prediction of Heart Disease Using Machine Learning Algorithms. Wavefront Compensation Technique for Terrestrial Line of Sight Free Space Optical Communication
×
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