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Accuracy Estimation

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Accuracy assessment is done to measure the agreement between a standard assumed to be correct and a classified image of unknown quality (Campbell, J.B. 2002). An adequate number of sample points representing different landuse categories were identified on the training data sets for accuracy estimation. A one-to-one comparison of the categories mapped from all the training datasets and the classified image was made and is listed in table 4. Accuracy estimation in terms of producer's accuracy, user's accuracy, overall accuracy and Kappa coefficient were subsequently made after generating confusion matrix. (Dwivedi et al., 2000).

The statistic is a measure of the difference between the actual agreement between reference data and an automated classifier and the chance agreement between the reference data and the random classifier as shown in equation (1) and equation (2). This statistics serves as an indicator of the extent to which the percentage correct values of an error matrix are due to “true” agreement versus “chance” agreement. It incorporates the nondiagonal elements of the error matrix as a product of the row and column diagonal. (Lillesand and Kiefer et al., 2000).

The producer’s accuracy, user’s accuracy corresponding to the various categories and overall accuracy were calculated and the results obtained are summarized in table 6.