Convolutional Attention in Ensemble With Knowledge Transferred for Remote Sensing Image Classification

Abstract

Ensemble learning is one of the hottest topics in machine learning. In this letter, we develop a convolutional attention in ensemble (CAE) method, which, for the first time, introduces attention-based weighting scheme into ensemble learning. The knowledge contained in base classifiers is transferred into the final classifier, by which the base classifier with a higher performance could be given much more attention. In particular, we employ convolutional attention models to develop an efficient ensemble classifier for image classification. Our CAE can leverage the representation capacity of convolutional neural networks to enhance the performance of ensemble classifiers. We apply our method to remote sensing image classification tasks, which achieves much better performance than the state of the arts.