Enhanced LiDAR Data Classification through Spectrum Transformer Networks

Light Detection and Ranging (LiDAR) technology, as an efficient data acquisition method, is widely used in various practical scenarios. Among them, the classification of LiDAR-generated gridded digital surface models (LiDAR-DSM) constitutes a key aspect of LiDAR data post-processing. However, how to efficiently extract long-range dependency features and high-frequency details from LiDAR data, especially their decisive role in accurately distinguishing land cover classes, remains an urgent problem to be solved. Traditional attention mechanisms perform well in local feature extraction but are inadequate in utilizing global contextual information, especially when dealing with elevation data with wide variation ranges and complex structures. In view of the above issues, this paper introduces an innovative method named the Spectrum-LiDAR Network, which is based on the Transformer encoder architecture and integrates a frequency-domain self-attention mechanism at its core. By implementing the Fourier transform, the data is converted to the frequency domain, thereby revealing and learning the complex dependency structures in LiDAR data that are difficult to intuitively recognize in the time domain. The Spectrum-LiDAR Network not only deepens the deep learning of elevation information but also significantly enhances the model's ability to recognize subtle differences in various terrains and land cover types through global frequency domain analysis, thereby greatly improving the performance and accuracy of LiDAR data classification. This provides a novel and efficient solution for the recognition of complex land features. Experimental evaluations demonstrate that the Spectrum-LiDAR Network can provide comparable or even superior classification results compared to the current state-of-the-art methods, further validating the effectiveness of this approach.