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article

Visualizing uncertainty in multi-spectral remotely sensed imagery

Authors:

Peter F. Fisher,

Jo WoodAuthors Info & Claims

Computers & Geosciences, Volume 28, Issue 3

Pages 337 - 350

https://doi.org/10.1016/S0098-3004(01)00051-6

Published: 01 April 2002 Publication History

Abstract

Error and uncertainty in remotely sensed data come from several sources, and can be increased or mitigated by the processing to which that data is subjected (e.g. resampling, atmospheric correction). Historically the effects of such uncertainty have only been considered overall and evaluated in a confusion matrix which becomes high-level meta-data, and so is commonly ignored. However, some of the sources of uncertainty can be explicitly identified and modelled, and their effects (which often vary across space and time) visualized. Others can be considered overall, but their spatial effects can still be visualized. This process of visualization is of particular value for users who need to assess the importance of data uncertainty for their own practical applications. This paper describes a Java-based toolkit, which uses interactive and linked views to enable visualization of data uncertainty by a variety of means. This allows users to consider error and uncertainty as integral elements of image data, to be viewed and explored, rather than as labels or indices attached to the data.

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Cited By

Kneusel RMozer M(2017)Improving Human-Machine Cooperative Visual Search With Soft HighlightingACM Transactions on Applied Perception10.1145/312966915:1(1-21)Online publication date: 18-Sep-2017
https://dl.acm.org/doi/10.1145/3129669
Hullman J(2016)Why Evaluating Uncertainty Visualization is Error ProneProceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization10.1145/2993901.2993919(143-151)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2993901.2993919
Demmans Epp CBull S(2015)Uncertainty Representation in Visualizations of Learning Analytics for Learners: Current Approaches and OpportunitiesIEEE Transactions on Learning Technologies10.1109/TLT.2015.24116048:3(242-260)Online publication date: 15-Sep-2015
https://dl.acm.org/doi/10.1109/TLT.2015.2411604
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Index Terms

Visualizing uncertainty in multi-spectral remotely sensed imagery
1. Applied computing
  1. Physical sciences and engineering
    1. Earth and atmospheric sciences

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Published In

cover image Computers & Geosciences

Computers & Geosciences Volume 28, Issue 3

April 2002

144 pages

ISSN:0098-3004

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Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2002

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Cited By

Kneusel RMozer M(2017)Improving Human-Machine Cooperative Visual Search With Soft HighlightingACM Transactions on Applied Perception10.1145/312966915:1(1-21)Online publication date: 18-Sep-2017
https://dl.acm.org/doi/10.1145/3129669
Hullman J(2016)Why Evaluating Uncertainty Visualization is Error ProneProceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization10.1145/2993901.2993919(143-151)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2993901.2993919
Demmans Epp CBull S(2015)Uncertainty Representation in Visualizations of Learning Analytics for Learners: Current Approaches and OpportunitiesIEEE Transactions on Learning Technologies10.1109/TLT.2015.24116048:3(242-260)Online publication date: 15-Sep-2015
https://dl.acm.org/doi/10.1109/TLT.2015.2411604
Ge YHexiang BLi S(2008)Geo-spatial Data Analysis, Quality Assessment and VisualizationProceeding sof the international conference on Computational Science and Its Applications, Part I10.1007/978-3-540-69839-5_20(258-267)Online publication date: 30-Jun-2008
https://dl.acm.org/doi/10.1007/978-3-540-69839-5_20
Lu DWeng Q(2007)A survey of image classification methods and techniques for improving classification performanceInternational Journal of Remote Sensing10.1080/0143116060074645628:5(823-870)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.1080/01431160600746456

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