从解密的 KH-9 历史卫星图像中探测越战炸弹坑

IF 5.7 Q1 ENVIRONMENTAL SCIENCES Science of Remote Sensing Pub Date : 2024-06-07 DOI:10.1016/j.srs.2024.100143
Philipp Barthelme , Eoghan Darbyshire , Dominick V. Spracklen , Gary R. Watmough
{"title":"从解密的 KH-9 历史卫星图像中探测越战炸弹坑","authors":"Philipp Barthelme ,&nbsp;Eoghan Darbyshire ,&nbsp;Dominick V. Spracklen ,&nbsp;Gary R. Watmough","doi":"10.1016/j.srs.2024.100143","DOIUrl":null,"url":null,"abstract":"<div><p>Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"10 ","pages":"Article 100143"},"PeriodicalIF":5.7000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000270/pdfft?md5=888e50ac2fde2e892dc3ae7418b930ae&pid=1-s2.0-S2666017224000270-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Detecting Vietnam War bomb craters in declassified historical KH-9 satellite imagery\",\"authors\":\"Philipp Barthelme ,&nbsp;Eoghan Darbyshire ,&nbsp;Dominick V. Spracklen ,&nbsp;Gary R. Watmough\",\"doi\":\"10.1016/j.srs.2024.100143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts.</p></div>\",\"PeriodicalId\":101147,\"journal\":{\"name\":\"Science of Remote Sensing\",\"volume\":\"10 \",\"pages\":\"Article 100143\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2024-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666017224000270/pdfft?md5=888e50ac2fde2e892dc3ae7418b930ae&pid=1-s2.0-S2666017224000270-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science of Remote Sensing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666017224000270\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of Remote Sensing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666017224000270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

每年都有成千上万的人因战争遗留爆炸物(包括未爆弹药(UXO)和被遗弃的弹药)而受伤。未爆弹药对受污染地区的生计和生态系统造成长期负面影响。剩余未爆炸弹药的确切位置往往是未知的,因为勘测和清理活动可能是危险、昂贵和耗时的。在越南、老挝人民民主共和国和柬埔寨,约有 20% 的土地仍受到越战遗留未爆弹药的污染。最近解密的历史 KH-9 卫星图像拍摄于越战期间和越战结束后不久,为绘制这些遗留污染的地图提供了机会。我们获取了东南亚两个研究区域的 KH-9 图像,并对其进行了正射影像处理。人工标注了图像子集中的炸弹坑,以训练用于自动弹坑检测的卷积神经网络(CNN)。卷积神经网络的 F1 分数达到 0.61,并在两个研究区域内识别出 50 多万个弹坑。与从解密的美国轰炸记录中获得的目标位置相比,检测到的弹坑提供了更精确的炸弹撞击位置信息。这有助于在非技术性勘测期间更精确地确定疑似危险区域的位置,以及更精细地确定未爆炸弹药的残余风险。该方法可直接应用于东南亚其他地区,由于 KH-9 图像成本低廉,且使用了开源软件,因此成本效益高。研究结果还表明,在最近的冲突中,有可能将弹坑探测纳入排雷行动的数据驱动决策中。
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
Detecting Vietnam War bomb craters in declassified historical KH-9 satellite imagery

Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts.

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
CiteScore
12.20
自引率
0.00%
发文量
0
期刊最新文献
Coastal vertical land motion across Southeast Asia derived from combining tide gauge and satellite altimetry observations Identifying thermokarst lakes using deep learning and high-resolution satellite images A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery A comprehensive evaluation of satellite-based and reanalysis soil moisture products over the upper Blue Nile Basin, Ethiopia A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment
×
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