基于增强种子区生长技术的卫星图像监督分类

Astha Baxi, Manoj Pandya, M. Kalubarme, M. Potdar
{"title":"基于增强种子区生长技术的卫星图像监督分类","authors":"Astha Baxi, Manoj Pandya, M. Kalubarme, M. Potdar","doi":"10.1109/NUICONE.2011.6153230","DOIUrl":null,"url":null,"abstract":"The image classifications techniques have been practiced by remote sensing experts following certain methods like unsupervised and supervised. Supervised classification requires precise human intervention to extract features. Enhanced Seeded region growing technique is an image segmentation method; where the image pixel is seeded by latitude and longitude recorded during ground truth data collection using GPS. The Enhanced seeded region growing technique generates clusters based upon 8 nearest neighbor pixel connections. Pattern recognition standard software is trained for the spectral signatures of the corresponding pixels. Then the supervised classification algorithm can be used. The system can leverage the potential of Location based services (LBS) and Information Communication Technology (ICT) to dynamically pull the latitude and longitude from the server using web services and gateway protocols. This method requires less effort to extract features from the image. This scheme is applied on satellite imagery covering surendranagar district in Gujarat, India.","PeriodicalId":206392,"journal":{"name":"2011 Nirma University International Conference on Engineering","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Supervised classification of satellite imagery using Enhanced seeded region growing technique\",\"authors\":\"Astha Baxi, Manoj Pandya, M. Kalubarme, M. Potdar\",\"doi\":\"10.1109/NUICONE.2011.6153230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The image classifications techniques have been practiced by remote sensing experts following certain methods like unsupervised and supervised. Supervised classification requires precise human intervention to extract features. Enhanced Seeded region growing technique is an image segmentation method; where the image pixel is seeded by latitude and longitude recorded during ground truth data collection using GPS. The Enhanced seeded region growing technique generates clusters based upon 8 nearest neighbor pixel connections. Pattern recognition standard software is trained for the spectral signatures of the corresponding pixels. Then the supervised classification algorithm can be used. The system can leverage the potential of Location based services (LBS) and Information Communication Technology (ICT) to dynamically pull the latitude and longitude from the server using web services and gateway protocols. This method requires less effort to extract features from the image. This scheme is applied on satellite imagery covering surendranagar district in Gujarat, India.\",\"PeriodicalId\":206392,\"journal\":{\"name\":\"2011 Nirma University International Conference on Engineering\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Nirma University International Conference on Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NUICONE.2011.6153230\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Nirma University International Conference on Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NUICONE.2011.6153230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

遥感图像分类技术已被遥感专家实践,主要采用无监督和有监督两种方法。监督分类需要精确的人工干预来提取特征。增强种子区域生长技术是一种图像分割方法;其中图像像素由使用GPS收集地面真实数据时记录的纬度和经度播种。增强的种子区域生长技术基于8个最近邻像素连接生成聚类。对模式识别标准软件进行相应像素光谱特征的训练。然后使用监督分类算法。该系统可以利用基于位置的服务(LBS)和信息通信技术(ICT)的潜力,使用web服务和网关协议动态地从服务器提取纬度和经度。这种方法从图像中提取特征所需的工作量较小。该方案在印度古吉拉特邦surendranagar地区的卫星图像上进行了应用。
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
Supervised classification of satellite imagery using Enhanced seeded region growing technique
The image classifications techniques have been practiced by remote sensing experts following certain methods like unsupervised and supervised. Supervised classification requires precise human intervention to extract features. Enhanced Seeded region growing technique is an image segmentation method; where the image pixel is seeded by latitude and longitude recorded during ground truth data collection using GPS. The Enhanced seeded region growing technique generates clusters based upon 8 nearest neighbor pixel connections. Pattern recognition standard software is trained for the spectral signatures of the corresponding pixels. Then the supervised classification algorithm can be used. The system can leverage the potential of Location based services (LBS) and Information Communication Technology (ICT) to dynamically pull the latitude and longitude from the server using web services and gateway protocols. This method requires less effort to extract features from the image. This scheme is applied on satellite imagery covering surendranagar district in Gujarat, India.
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
Optimal placement of power system stabilizers: Simulation studies on a test system Exploring a new direction in colour and texture based satellite image search and retrieval system Performance evaluation of IEEE 802.16e WiMax physical layer ANN controller for binary distillation column — A Marquardt-Levenberg approach ANN based sensorless rotor position estimation for the Switched Reluctance Motor
×
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