Performance comparison of two pooling strategies for remote sensing image scene classification

With the advances of convolutional neural networks (CNNs), the accuracy of remote sensing image scene classification has been greatly boosted thanks to the powerful features extracted through CNNs. Although significant success has been achieved, most of existing methods are dominated by the use of fully-connected CNN features. This paper focuses on the performance comparison of two kinds of novel pooling strategies, including generalized max pooling (GMP) and task-driven pooling (TDP), for remote sensing image scene classification. To this end, an off-the-shelf CNN model is used as backbone network to extract multi-scale convolutional features. Then, GMP and TDP are respectively adopted to obtain globally pooled features. Finally, scene classification is performed with support vector machine (SVM). In the experiment, we evaluate the performance of these two kinds of pooling schemes on a widely-used scene classification benchmark data set. The experimental results show that (i) using pooled CNN convolutional features can obtain better results than using fully-connected CNN features and (ii) TDP is slightly better than GMP.