Exploring uncertainty in remotely sensed data with parallel coordinate plots.
Yong, G and Sanping, L and Lakhan, C and Lucieer, A (2009) Exploring uncertainty in remotely sensed data with parallel coordinate plots. International Journal of Applied Earth Observation and, 11 (6). pp. 413-422. ISSN 1569-8432
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Official URL: http://dx.doi.org/10.1016/j.jag.2009.08.004
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
fuzzymembership 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.
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|Keywords:||Parallel coordinate plots (PCP)
Remotely sensed data
|Deposited By:||Miss AM Young|
|Deposited On:||14 Jul 2010 12:22|
|Last Modified:||14 Jul 2010 12:22|
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