Computer processing of remotely-sensed images: an introduction
Computer processing of remotely-sensed images: an introduction
Exploratory cartographic visualization: advancing the agenda
Computers & Geosciences - Special issue on exploratory cartographic visualization
Exploring spatial data representation with dynamic graphics
Computers & Geosciences - Special issue on exploratory cartographic visualization
Visualizing spatial data uncertainty using animation
Computers & Geosciences - Special issue on exploratory cartographic visualization
Modelling and visualizing multiple spatial uncertainties
Computers & Geosciences - Special issue on exploratory cartographic visualization
Dynamic display of spatial data-reliability: does it benefit the map user?
Computers & Geosciences - Special issue on exploratory cartographic visualization
Visual exploration of uncertainty in remote-sensing classification
Computers & Geosciences - Special issue on computers, geoscience and geocomputation
Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
Geo-spatial Data Analysis, Quality Assessment and Visualization
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
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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.