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BEYOND ACCURACY ASSESSMENT: 
CORRECTION OF MISCLASSIFICATION 
Nicholas R. Chrisman 
University of Wisconsin 
Madison, WI 53706 
ABSTRACT 
Map accuracy analysis is critical to remote sensing, but 
currently accepted techniques, such as percentage correctly 
classified, provide ambiguous information and rest on shaky 
statistical foundations. Cohen's kappa, a measure of 
agreement, provides a more comparable index of 
classification success. Map accuracy data should be applied 
to more than accuracy assessment; it provides the basis for 
correcting area estimates. This paper demonstrates 
Tenenbein's double sampling method for correcting errors in 
classification, a simple manipulation of conditional 
probabilities. Results from published data show substantial 
changes in area estimates. 
BACKGROUND 
The assessment of accuracy has become a common topic in 
studies of remote sensing. Unfortunately, this work seems 
divided into two distinct populations. One set grapples the 
statistical questions of designing samples, but seems 
removed from actual data problems. The other set is firmly 
based in application projects, but not particularly 
conversant with sophisticated analysis. Information 
generated in applied accuracy studies is not fully utilized. 
Furthermore, research in remote sensing seems isolated from 
similar work in statistics. There is a need to pull these 
strands together to ensure better tools for application 
projects. This paper will attempt to reevaluate the methods 
available and to contribute some further possibilities. 
METHODOLOGICAL WORK IN ACCURACY ANALYSIS 
The question of overall map accuracy was addressed in a 
fairly comprehensive manner by Hord and Brooner (1976). 
They decomposed the overall problem into errors at control 
points, along boundary lines, and for classification of 
areas. This general approach is useful beyond remote 
sensing circumstances, but concern in this field has been 
focussed almost exclusively on the area case. Either point 
and line errors are considered smaller than the 
classification errors, or, perhaps, the point and line 
errors are bundled into a technology which must be primarily 
judged by its classification accuracy. 
Hord and Brooner emphasize the probabilistic aspect of 
classification error and suggest reporting the percentage 
correct with a statistically parametric confidence interval. 
They recognize the binomial distribution for true-false 
data, but regard the normal approximation as adequate for