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	Exploring uncertainty in remotely sensed data with parallel coordinate plots
	Ge Y.
	2009
关键词	Parallel coordinate plots (PCP) Remotely sensed data Shannon's entropy Uncertainty Interactive visualization Brushing classifications visualization exploration imagery
英文摘要	The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta. Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels. (c) 2009 Elsevier B.V. All rights reserved.
出处	International Journal of Applied Earth Observation and Geoinformation
卷	11
期	6
页	413-422
收录类别	SCI
语种	英语
ISSN号	0303-2434
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/23945]
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.. Exploring uncertainty in remotely sensed data with parallel coordinate plots. 2009.

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