From Spatial to Frequency Domain: A Pure Frequency Domain FDNet Model for the Classification of Remote Sensing Images

In remote sensing scene classification (RSSC), a common approach to reduce processing costs is to downsample images in the spatial domain to lower resolution. However, this approach can lead to the loss of texture information. Conversely, the frequency domain analysis can effectively extract frequency variations of pixel values, aiding in distinguishing texture differences in remote sensing scenes. Therefore, it is common in RSSC to use frequency domain analysis as an auxiliary branch of neural networks to extract additional information. The essence of these approaches is to extract feature information from both the spatial and frequency domains and then fuse them together. However, this combination method does not fully leverage the benefits of frequency domain feature extraction. The approach of extracting feature information solely from the frequency domain may require further research and experimentation to explore its potential advantages and applications. This study integrates wavelet transform into downsampling operations and creatively designs a network model called the frequency domain deep learning network (FDNet), which uses frequency domain methods as the backbone of a neural network and performs feature extraction solely in the frequency domain. FDNet not only solves the problem of information loss caused by spatial downsampling but also provides a new research direction of combining frequency domain methods with neural networks, that is, using frequency domain methods as the backbone of neural networks. FDNet also provides a new research direction for RSSC, which is to extract features only in the frequency domain. FDNet has been evaluated on three public remote sensing datasets: SIRI-WHU, RSSCN7, and AID. Each dataset underwent three experiments, with average classification accuracies of 97.78%, 97.62%, and 95.92%, respectively. The results show that FDNet outperforms other advanced methods participating in the comparison of classification tasks.