CSDS: End-to-End Aerial Scenes Classification With Depthwise Separable Convolution and an Attention Mechanism

Compared with natural scenes, aerial scenes are usually composed of numerous objects densely distributed within the aerial view, and thus, more key local semantic features are needed to describe them. However, when existing CNNs are used for remote sensing image classification, they typically focus on the global semantic features of the image, and especially for deep models, shallow and intermediate features are easily lost. This article proposes a channel-spatial attention mechanism based on a depthwise separable convolution (CSDS) network for aerial scene classification to solve these challenges. First, we construct a depthwise separable convolution (DS-Conv) and pyramid residual connection architecture. DS-Conv extracts features from each channel and merges them, effectively reducing the number of necessary calculations, and the pyramid residual connections connect the features from multiple layers and create associations. Then, the channel-spatial attention algorithm causes the model to obtain more effective features in the channel and spatial domains. Finally, an improved cross-entropy loss function is used to reduce the impact of similar categories on backpropagation. Comparative experiments on three public datasets show that the CSDS network can achieve results comparable to those of other state-of-the-art methods. In addition, visualization of feature extraction results by the Grad-CAM algorithm and ablation experiments for each module reflect the powerful feature learning and representation capabilities of the proposed CSDS network.

Automated summary

This article proposes a channel-spatial attention mechanism based on a depthwise separable convolution (CSDS) network for aerial scene classification. Experimental results on three public datasets show that the CSDS network achieves comparable performance to other state-of-the-art methods. Visualization of feature extraction results and ablation experiments demonstrate the powerful feature learning and representation capabilities of the proposed CSDS network.