Improved AlexNet and embedded multi-attention for remote sensing scene image classification

In order to improve the overall accuracy (OA) of the AlexNet model for high-resolution remote sensing scene images with complex backgrounds, we proposed an improved remote sensing scene image classification model. Firstly, we used Layer Normalization (LN) to replace the Local Response Normalization (LRN) in AlexNet and changed the convolution kernel of the first convolution layer to 7 × 7. Secondly, to focus on critical information in the feature extraction process, and suppress irrelevant background information, the two attention modules of Convolution Block Attention Module (CBAM) and Squeeze and Excitation Module (SEM) were combined. In this study, the classification verification was performed on three remote sensing scene datasets of NWPU-RESISC45, AID, and UCM, and achieved 96.29%, 96.02%, and 96.57% overall accuracy, respectively. Compared with AlexNet, the OA improved by 14.38%, 12.09%, and 9.9%, respectively, therefore, the improved model of this study can significantly distinguish between object information and background information in remote sensing scene imagery.