SCAFFNet: A novel object classification algorithm in low-altitude scenarios for eVTOL

The classification of aerial images can provide crucial data sources for Earth observation. Although conventional neural network classification models based on large sample training have made significant strides in accuracy, the high costs of obtaining large samples and training are constraints on the widespread application of these methods. Inspired by few-shot learning methods, this paper proposes SCAFFNet (Spatial-Channel Attention and Fourier Transform Fusion Network) to address the issue of few-shot remote sensing scene image classification. By combining attention mechanisms and Fourier transform techniques, SCAFFNet enhances feature extraction and classification performance with limited data samples. Experiments on the UC Merced and WHU-RS19 datasets validate the effectiveness of SCAFFNet, demonstrating that the proposed method improves classification accuracy compared to traditional methods and showing its potential for practical applications in remote sensing image analysis.