New classification method for remotely sensed imagery via multiple-point simulation: Experiment and assessment

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	New classification method for remotely sensed imagery via multiple-point simulation: Experiment and assessment
	Ge Yong
	2008
关键词	Maximum likelihood
英文摘要	There has been substantial effort dedicated to the issue of how to incorporate spatial information to improve the classification accuracy in past decades and some excellent methods have been developed. Each method has its own advantages and disadvantages for different images and user requirements. This paper proposes a new classification method, which introduces multiple-point simulation to improve the classification of remotely sensed imagery data by incorporating structural information through a training image. This new method named CCSSM is the derivation of two classifications and based on spectral and spatial information, which then are fused. For validation purpose, a real-life example of road extraction from Landsat TM is used to substantiate the conceptual arguments. An assessment of the accuracy of the proposed method compared with results using a maximum likelihood classifier shows the overall accuracy improves from 48.9% to 82.6%, and the kappa coefficient improves from 0.12 to 0.55 and therefore, the new method has superior overall performance on the classification of remotely sensed data. © 2008 Society of Photo-Optical Instrumentation Engineers.
出处	Journal of Applied Remote Sensing
卷	2 期:1
收录类别	EI
语种	英语
内容类型	EI期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/24905]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Ge Yong. New classification method for remotely sensed imagery via multiple-point simulation: Experiment and assessment. 2008.