X. Zhuang et al., ANALYSIS OF CLASSIFICATION RESULTS OF REMOTELY-SENSED DATA AND EVALUATION OF CLASSIFICATION ALGORITHMS, Photogrammetric engineering and remote sensing, 61(4), 1995, pp. 427-433
Classification results of remotely sensed data are usually summarized
as confusion matrices, and various classification algorithms are used
to improve results. Confusion matrices should be normalized to assess
classification accuracies of remotely sensed data, and multiple compar
isons are required to evaluate the classification algorithms. The clas
sical iterative proportional fitting procedure, including eliminating
zero counts, was scrutinized to normalize confusion matrices. The Tuke
y multiple comparison method was used for the comparison of results fr
om three classification algorithms: minimum distance, maximum likeliho
od, and an artificial neural network. Normalized confusion matrices pr
ovided uniform margins and accuracies for each classification category
. The Tukey comparisons of the three algorithms were made simultaneous
ly; results provided the overall classification accuracy for each algo
rithm and showed no differences among the algorithms at a risk level o
f 5 percent. Normalized confusion matrices can be compared entry by en
try because of their uniform margins. Results of this study indicate t
hat classification algorithms can be evaluated with the Tukey method,
and the multiple comparisons of the algorithms should be made based on
normalized category accuracies obtained with the iterative proportion
al fitting procedure. Normalized confusion matrices provide a unified
measure of producer's and user's accuracies.