Remote Sensing Image Scene Classification Based on Multidimensional Attention and Feature Enhancement

Remote sensing image scene classification is a challenging task that involves automatically assigning labels to remote sensing images based on predefined categories. The inherent intra-class diversity and inter-class similarity of remote sensing images make it difficult for classification models to capture the discriminative key information necessary for accurate labeling, resulting in classification confusion. This paper proposes a novel method called Multidimensional Attention and Feature Enhancement (MA-FE) to address this issue. The proposed MA-FE method comprehensively captures essential features in different dimensions of channel and position through the Multidimensional Attention (MA) module, which integrates and combines the captured features. The Feature Enhancement (FE) module then amplifies the discriminative features to suppress the interference of useless information, thus improving the representation ability of the model. We conducted detailed experiments on three public remote sensing datasets and performed a comparative evaluation with multiple remote sensing scene classification methods proposed in recent years. The overall accuracies of the proposed MA-FE method on these datasets were 99.66%, 95.68%, and 93.21%, respectively. Our experimental results demonstrate that the proposed MA-FE method is more effective in extracting complex features in remote sensing images than other methods, thereby proving its effectiveness.