LAHNet: A Convolutional Neural Network Fusing Low- and High-Level Features for Aerial Scene Classification

In this paper, we proposed an innovative end-to-end convolutional neural network (CNN), which is trained to learn how to fuse multi-level features for aerial scene classification. Instead of using only coarse semantic features as conventional CNNs, we resort to first hierarchically extracting dense high-level features and then element-wise fusing them with low-level features to build a comprehensive feature representation, which contains not only high-level semantic information but also fine-grained low-level details, for scene classification. The network is evaluated on two broadly used aerial scene datasets, UCM and AID. The experimental results indicate that the proposed LAHNet performs superiorly compared to the existing benchmark methods. Furthermore, visualization of the fused features presents an intuitive illustration of the remarkable improvement.