Feature extraction of typical vegetation based on rapid eye images

	Feature extraction of typical vegetation based on rapid eye images
	Ma, Xingye 1; Liu, Shaochuang 1; Ma, Youqing 1; Yanghuan 1
刊名	Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia
	2016
卷号	39 期号:3 页码:380-387
英文摘要	High-resolution images can provide more features about spatial, geometry and texture.Traditional pixel-based classification method only used the spectral information of images, without considering the space and texture information.Aiming at high-resolution remote sensing image RapidEye has abundant texture, structure and spatial features, this paper based on surface feature elements,combined with multi temporal and multi sensors, used object-oriented classification -support vector machine (SVM) to extract surface elements information.Using feature selection to extract single feature type such as vegetation types and finding out a group features that can make the overall effect ideal,analyzing the accuracy and influencing factors of various classes by accuracy evaluation.Results show that with 26 features of optimized added, the overall classification accuracy improved than the traditional classification methods, SVM classification precision can reach 91.9%,Kappa coefficient is 0.903 and the experimental result is ideal.
收录类别	EI
语种	英语
WOS记录号	WOS:20162502506095
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39600]
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. School of Information, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, Beijing 2.100094, China
推荐引用方式 GB/T 7714	Ma, Xingye,Liu, Shaochuang,Ma, Youqing,et al. Feature extraction of typical vegetation based on rapid eye images[J]. Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia,2016,39(3):380-387.
APA	Ma, Xingye,Liu, Shaochuang,Ma, Youqing,&Yanghuan.(2016).Feature extraction of typical vegetation based on rapid eye images.Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia,39(3),380-387.
MLA	Ma, Xingye,et al."Feature extraction of typical vegetation based on rapid eye images".Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 39.3(2016):380-387.