Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery

Ranga Raju Vatsavai
{"title":"Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery","authors":"Ranga Raju Vatsavai","doi":"10.1145/2487575.2488210","DOIUrl":null,"url":null,"abstract":"In this paper, we present a computationally efficient algorithm based on multiple instance learning for mapping informal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classifiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contiguous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experiments on very high-resolution satellite imagery, representing four unique geographic regions across the world. Our method showed consistent improvement in accurately identifying informal settlements.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"25 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2487575.2488210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 36

Abstract

In this paper, we present a computationally efficient algorithm based on multiple instance learning for mapping informal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classifiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contiguous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experiments on very high-resolution satellite imagery, representing four unique geographic regions across the world. Our method showed consistent improvement in accurately identifying informal settlements.
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
高斯多实例学习方法用于使用非常高分辨率的图像绘制世界贫民窟
在本文中,我们提出了一种基于多实例学习的计算效率高的算法,用于利用高分辨率遥感图像绘制非正式住区(贫民窟)。从遥感角度来看,非正式住区具有独特的空间特征,使其区别于工业、商业和正式住区等其他城市结构。然而,常规的模式识别和机器学习方法,主要是单实例或逐像素分类器,往往不能准确地绘制非正式住区,因为它们不能捕捉复杂的空间模式。为了克服这些限制,我们采用了基于多实例的机器学习方法,其中连续像素组(图像补丁)由高斯分布生成。我们在非常高分辨率的卫星图像上进行了几次实验,代表了世界上四个独特的地理区域。我们的方法在准确识别非正式定居点方面显示出持续的改进。
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
A general bootstrap performance diagnostic Flexible and robust co-regularized multi-domain graph clustering Beyond myopic inference in big data pipelines Constrained stochastic gradient descent for large-scale least squares problem Inferring distant-time location in low-sampling-rate trajectories
×
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