Classification of multispectral images in coral environments using a hybrid of classifier ensembles

The accuracy attained in the mapping of underwater areas is limited by the effect of variations in the water column, which degrade the signal received by the orbital sensor, creating interclasses confusion that introduce errors into the final result of the classification process. In this article we will describe a hybrid classifier ensembles; the classification is done by progressive refining in three stages. At the end of this process, a combining unit links the various partial classifications generated and achieve the accuracy level desired. At the end, the result obtained by the ensemble is compared to the results achieved by the application of multi-class voting scheme methods based on support vector machine: One-Against-the-Rest and One-Against-One. Classification accuracy showed the viability and the potential of using the proposed ensemble to classify images.