Comparison of Adaboost. M2 and Perspective Based Model Ensemble in Multispectral Image Classification

AdaBoost is a popular ensemble method utilized in pattern recognition problems that are considered tough. Besides being a robust technique it does suffer from few limitations viz. size of training data and presence of noise in training data. In this context, we proposed a novel technique called Perspective Based Model (PBM) for ensemble creation in case of multispectral data analysis. In the present paper, we evaluate its performance in terms of classification accuracy against AdaBoost. M2. Preliminary results show higher accuracy through PBM compared to a single classifier but also a lower classification performance for PBM compared to AdaBoost. M2. An improved performance is also observed for PBM on adding new data features.