A New Method for Accuracy Assessment of Sub-Pixel Classification Results

Problem statement: Classification accuracy assessment is the main stage of information extraction to evaluate the performance of a classifier. According to the output's classifiers (thematic map/fraction map), a properly strategy for accuracy assessment should be taken into consideration. Since pure pixels are used in traditional accuracy assessment of full pixel classifiers they are not suitable for assessment of sub-pixel classifiers. The objectives of this study were to find a standard sub-pixel accuracy assessment method for evaluation of the sub-pixel classifiers. For this purpose many efforts had been taken and recently some methods and measures such as entropy and cross-entropy had been proposed for sub-pixel accuracy assessment. These methods had their own shortcomings which seriously a fuzzy ground truth data set was needed, the matter that is not available simply. Approach: In this study recently sub-pixel classifier accuracy assessment methods were explored and a new method based on correctness coefficient parameter for the sub-pixel accuracy assessment was introduced. In order to evaluate the CC method, a sub-image of the AVIRIS of hyper spectral data was taken over an agricultural area of California, USA in 1994. The study area consisted of 16 classes. Sub-pixel accuracy assessment methods were discussed. The experiment results using AVIRIS data demonstrated the ability of the new accuracy assessment method. Results: Indeed, in proposed method the matching rate of fraction maps with ground truth data was quantified as correctness coefficient parameters. As a result, flexibility and consistency of sub-pixel accuracy assessment certified by correctness coefficient regarding the type of available data and classification methods. The obtained overall CC over LSU method using 120 bands is about 84.9%. In contrast, the obtained results in terms of OA and Kappa coefficient over LSU method which were achieved by maximum value rule on the fraction maps are 86 and 84% respectively. The Kappa coefficient value is close to overall CC of LSU method. Conclusion: Hence, evaluation and experiments demonstrated that the CC method as an accuracy assessment parameter of a soft classifier can be substituted reasonably by traditional accuracy parameters.