An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan

	An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan
	Wang, Jian-Jun 1; Zhou, Guisheng 1; Zhang, Yun 1; Bussink, Coen 1; Zhang, Jiahua 1; Ge, Hao 1
刊名	Remote Sensing Letters
	2016
卷号	7 期号:10 页码:945-954
关键词	SPATIAL-RESOLUTION REQUIREMENTS OBJECT-BASED CLASSIFICATION SUPPORT VECTOR MACHINES REMOTE-SENSING DATA MODIS TIME-SERIES LAND-COVER MAPS SATELLITE DATA ACCURACY ASSESSMENT TEMPORAL WINDOWS DISCRIMINATION
通讯作者	Wang, Jian-Jun (wangjianjun@yzu.edu.cn)
英文摘要	ABSTRACT: Remote sensing has special advantages to monitor drug production that causes serious problems to global society. The widely used high spatial resolution images are too costly to make the full coverage of the opium poppy fields in a large area. Although the hyperspectral imagery acquired by Earth Observing-1 (EO-1) Hyperion that is free with medium spatial resolution has been employed, the used unsupervised multiple endmember spectral mixture analysis (MESMA)-based method is time-consuming for a large area. The present study used an unsupervised mixture-tuned matched filtering (MTMF)-based method to detect poppy fields from a Hyperion image covering a study area in Helmand, Afghanistan, and it achieved the producer’s, user’s and overall accuracies of 61%, 73% and 76%, as well as the kappa coefficient of 0.48. This method worked over 10 times faster than the MESMA-based method with similar detection accuracies. This MTMF-based method provides a potential alternative for the United Nations and the Afghanistan government to monitor opium poppy cultivation in Afghanistan. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
学科主题	Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20163102656930
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39548]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China 2. Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, Canada 3. Joint International Research Laboratory of Agriculture and Agricultural Product Safety, Yangzhou University, Yangzhou, China 4. Statistics and Surveys Section, United Nations Office on Drugs and Crime (UNODC), Vienna, Austria 5. Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Wang, Jian-Jun,Zhou, Guisheng,Zhang, Yun,et al. An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan[J]. Remote Sensing Letters,2016,7(10):945-954.
APA	Wang, Jian-Jun,Zhou, Guisheng,Zhang, Yun,Bussink, Coen,Zhang, Jiahua,&Ge, Hao.(2016).An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan.Remote Sensing Letters,7(10),945-954.
MLA	Wang, Jian-Jun,et al."An unsupervised mixture-tuned matched filtering-based method for the remote sensing of opium poppy fields using EO-1 Hyperion data: an example from Helmand, Afghanistan".Remote Sensing Letters 7.10(2016):945-954.