Uncertainty characterization in remotely sensed land cover information

Jingxiong Zhang 1 and Jiabing Sun 2
1 School of Remote Sensing, Wuhan University
LIESMARS – Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing
129 LuoYu Road, Wuhan 430079, China
Tel: +086 27 63187371; Fax: + 086 27 68778086 
jxzhang@whu.edu.cn
2 School of Remote Sensing, Wuhan University
129 LuoYu Road, Wuhan 430079, China

Abstract
Uncertainty characterization has become increasingly recognized as an integral component in thematic mapping based on remotely sensed  imagery, and descriptors such as percent correctly classified pixels (PCC) and Kappa coefficients of agreement have been devised as thematic accuracy metrics. However, such  spatially averaged measures about accuracy neither offer hints about spatial variation in  misclassification, nor are they useful for quantifying error margins in derivatives, such as areal extents of different land cover types and land cover change statistics. Such limitations originate from  the deficiency  that spatial dependency is not accommodated in the conventional methods for error analysis. Geostatistics provides a good framework for uncertainty characterization in land cover information. Methods for predicting and propagating misclassification will be developed on the basis of indicator samples and covariates, such as  spectrally derived posteriori probabilities. Experiment using simulated data sets was carried out to quantify error in land cover change derived from post-classification comparison. It was found that  significant biases result from applying joint probability rules assuming temporal independence between misclassifications across time, thus consolidating the need for stochastic simulation in error modeling. Further investigations are anticipated incorporating indicators and probabilistic data for mapping and propagating misclassification. 

Keywords: geostatistics, land cover change, misclassification, stochastic simulation

In: Caetano, M. and Painho, M. (eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 5 – 7 July 2006, Lisboa, Instituto Geográfico Português

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