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	Assessing the quality of training data in the supervised classification of remotely sensed imagery: a correlation analysis
	Ge Y.
	2012
关键词	quality assessment of training data rough set measures correlation analysis accuracy assessment land-cover scale map
英文摘要	Training data play an important role in the supervised classification process of remotely sensed images. Its quality is an important factor affecting the accuracy of image classification. Therefore, measuring the quality of training data is essential for classification procedures and subsequent operations. This paper discusses a new method for the quality assessment of training data before the classification procedure and investigates the correlation between measures for training data and measures for classified images at category and image level, respectively. Five groups of sample data collected from a Landsat TM image were used in correlation analyses. The results demonstrate that the proposed method is valid for measuring the quality of training data and can, to some extent, reflect the quality of classified images which are obtained through supervised classification with the corresponding training dataset.
出处	Journal of Spatial Science
卷	57
期	2
页	135-152
收录类别	SCI
语种	英语
ISSN号	1449-8596
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/30872]
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 Y.. Assessing the quality of training data in the supervised classification of remotely sensed imagery: a correlation analysis. 2012.

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