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Exploring uncertaintly in remotely sensed data with parallel coordinate plots

Citation

Yong, G and Sanping, L and Lakhan, C and Lucieer, A, Exploring uncertaintly in remotely sensed data with parallel coordinate plots, International Journal of Applied Earth Observation and Geoinformation, 11, (6) pp. 413-422. ISSN 1569-8432 (2009) [Refereed Article]


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DOI: doi:10.1016/j.jag.2009.08.004

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:Parallel coordinate plots (PCP), remotely sensed data, Shannon's entropy, Uncertainty, Interactive visualisation, Brushing
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Environment
Objective Group:Ecosystem Assessment and Management
Objective Field:Ecosystem Assessment and Management not elsewhere classified
Author:Lucieer, A (Associate Professor Arko Lucieer)
ID Code:60304
Year Published:2009
Web of Science® Times Cited:7
Deposited By:Geography and Environmental Studies
Deposited On:2010-01-29
Last Modified:2010-04-14
Downloads:0

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