Deep Network Ensembles for Aerial Scene Classification

Abstract

It is well known in the machine learning community that the ensembles of neural networks outperform the respective individual networks; hence recent work on aerial scene classification has been trending toward various network fusion strategies. However, training multiple deep networks can be computationally expensive. Snapshot ensembling has recently appeared as an alternative and claims to provide the performance of network ensembles at the cost of a single network's training. In this letter, we present the results of a comparative study on deep network ensembles in the context of aerial scene classification, where homogeneous, heterogeneous, and snapshot ensembling strategies are explored with both DenseNet and Inception networks; contrary to the existing work, we employ model fusion at the last convolutional layer's level. The explored approaches are validated with the two largest and most challenging data sets available (AID and NWPU-RESISC45), and state-of-the-art results are achieved on both.