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Class Decomposition Approaches for Land Cover Classification: A Comparative Study

Several class decomposition approaches are proposed in literature and found to perform well with varying degree of success in comparison to a multiclass classification results. Especially, the error correcting output codes based approaches are found to work quite well due to their better error recovering capabilities (1). Recently (2) proposed that ensemble of nested dichotomies based approaches can also be used for class decomposition purpose and found to work well in comparison to exhaustive error correcting output codes. These paper presents the results obtained by using error correcting output codes (exhaustive code approach) and ensemble of nested dichotomies based class decomposition approaches for remote sensing data using a back propagation neural network as base classifier. Two training data sets having a total of 2700 and 700 pixels from seven land cover classes are used while a total of 2037 pixel was used as test data. To judge the performance of both approaches with noisy data, two data sets having 20% and 40% noise in the training data set of 2700 pixels were used. Results from this study suggest a comparable performance by ensemble of nested dichotomies to that of exhaustive error corrected output codes based class decomposition approach for both large and small training data sets. Further, exhaustive error corrected output codes based class decomposition approach provide slightly better (not statistically significant) results with noisy data set in comparison to the ensemble of nested dichotomies approach. Thus suggesting that ensemble of nested dichotomies is effective in error recovering capabilities as compared to ECOC approach and uses about 1/2 of computational time in comparison.