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Active learning (AL) technique is the classification of remote sensing images, where collecting efficient training data is costly in terms of labour and the time taken. The prime objective of AL technique is to obtain high classification accuracy with the training sample as compact as possible. Most studies on the classification of remote sensing images using AL, focused only on accuracy, with hardly any study on computation time. Keeping reduction of computation time as the objective, here we present, an entropy query by bagging (EQB)-based AL approach in the extreme learning machine (ELM) framework for remote sensing image classification. The performance of this approach is compared with the widely used support vector machine (SVM) AL framework in combination with different query strategies. To verify the efficacy of the study, the approaches were tested on two hyperspectral remote-sensing images, i.e. Kennedy Space Centre (KSC) and Botswana (BOT). The proposed system depicts competitive classification performance while significantly reducing computation time. [ABSTRACT FROM AUTHOR]