Hyperspectral and LiDAR Classification via Frequency Domain-Based Network

Local-global feature learning method based on deep learning has significantly improved the collaborative classification of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data. However, HSI encompasses numerous bands with significant interband correlations. Addressing how to efficiently capture spatial, spectral, and elevation information from hyperspectral and LiDAR data while considering data redundancy and enhancing feature representation of land cover will contribute to enhancing classification effectiveness. Combining frequency feature learning methods with convolutional neural networks (CNNs), transformers, and other architectures to construct an end-to-end feature learning network framework is an effective method. Therefore, this article proposes a frequency domain-based network (FDNet) for the classification of HSI and LiDAR data using a frequency local feature learning framework and a self-attention mechanism based on fast Fourier transform (FFT). FDNet could effectively capture local efficient frequency features of spatial, spectral, and elevation information in HSI and LiDAR data in an adaptive feature learning style, and embedding convolutional offsets into the frequency domain-based transformer network not only enhances local features but also effectively captures global semantic characteristics of land covers while reducing computational complexity. We validated the efficacy of FDNet across three publicly available datasets and a particularly challenging self-constructed dataset, denoted as the Yancheng dataset. The source codes will be available at https://github.com/RSIP-NJUPT/FDNet.