Modeling and visualizing uncertainty in digital thematic maps

M. S. Ganesh Prasad; M. K. Arora; V. K. Sajith

doi:10.1117/12.693820

22 December 2006 Modeling and visualizing uncertainty in digital thematic maps

M. S. Ganesh Prasad, M. K. Arora, V. K. Sajith

Proceedings Volume 6405, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications; 64052E (2006) https://doi.org/10.1117/12.693820
Event: SPIE Asia-Pacific Remote Sensing, 2006, Goa, India

Abstract

Spatial data in the form of thematic maps produced from remote sensing images are widely used in many application areas such as hydrology, geology, disaster management, forestry etc. These maps inherently contain uncertainties due to various reasons. The presence of uncertainty in thematic maps degrades the quality of maps and subsequently affects the decisions based on these data. Traditional way of quantifying quality is to compute the overall accuracy of the map, which however does not depict the spatial distribution of quality of whole map. It would be more expedient to use pixel-wise uncertainty as a means of quality indicator of a thematic map. This can be achieved through a number of mathematical tools based on well known theories of probability, geo-statistics, fuzzy sets and rough sets. Information theory and theory of evidence may also be adopted in this context. Nevertheless, there are several challenges involved in characterizing and providing uncertainty information to the users through these theories. The aim of this paper is to apprise the users of remote sensing about the uncertainties present in the thematic maps and to suggest ways to adequately deal with these uncertainties through proper modeling and visualization. Quantification and proper representation of uncertainty to the users may lead to increase in their confidence in using remote sensing derived products.

Citation Download Citation

M. S. Ganesh Prasad, M. K. Arora, and V. K. Sajith "Modeling and visualizing uncertainty in digital thematic maps", Proc. SPIE 6405, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications, 64052E (22 December 2006); https://doi.org/10.1117/12.693820

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KEYWORDS

Visualization

Fuzzy logic

Image classification

Remote sensing

Data modeling

Probability theory

Image processing

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