QQ客服 官方微博 反馈留言

	LOCAL SPATIAL ANALYSIS IN SURFACE INFORMATION EXTRACTION OF COAL MINING AREAS WITH HIGH FRACTIONAL VEGETATION COVER USING MULTI-SOURCE REMOTE SENSING DATA
	Wang, Nan ; Du, Chen ; Qin, Qi Ming
	2013
关键词	Local spatial analysis multi-source remote sensing coalfield Supporting Vector Machine (SVM) surface anomaly extraction CLASSIFICATION ASSOCIATION STATISTICS
英文摘要	The objective of the study is to utilize the local spatial statistics in multi-source remote sensing to analyze and extract surface anomalies in coal mining areas. We illustrated the equations and characteristics of three local spatial statistics, and then calculated the textual bands of them. In contrast with the selected optimal bands, the local spatial analysis improved the classification accuracy from 93% up to 98% based on Supporting Vector Machine (SVM) Classification. In addition, a few Ground Truth Region of Interests (ROIs) were also derived in the multi-spectral image. By means of the hyper-spectral remotely sensed image covering the ROIs, we directly identified six different surface objects or anomalies and inferred that a clustering of minerals and sandy soil with dense vegetation was a developing coalfield, which should be verified in the ground survey.; Engineering, Electrical & Electronic; Geosciences, Multidisciplinary; Remote Sensing; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1109/IGARSS.2013.6723361
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/311868]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Wang, Nan,Du, Chen,Qin, Qi Ming. LOCAL SPATIAL ANALYSIS IN SURFACE INFORMATION EXTRACTION OF COAL MINING AREAS WITH HIGH FRACTIONAL VEGETATION COVER USING MULTI-SOURCE REMOTE SENSING DATA. 2013-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。