Multiscale 3-D–2-D Mixed CNN and Lightweight Attention-Free Transformer for Hyperspectral and LiDAR Classification

The effective combination of hyperspectral image (HSI) and light detection and ranging (LiDAR) data can be utilized for land cover classification. Recently, deep learning-based classification methods, especially those utilizing Transformer networks, have achieved remarkable success. However, deep learning classification methods for multi-source data still encounter various technical challenges, such as the comprehensive utilization of multi-scale information, the lightweight network design, and the efficient fusion strategies for heterogeneous data. To address these challenges, we propose a novel and efficient deep neural network, namely multi-scale 3D-2D mixed CNN feature extraction and multi-source data lightweight attention-free fusion network (M2FNet) based on CNN and Transformer. Through end-to-end training, this network effectively combines heterogeneous information from multiple sources, leading to improved performance in joint classification. Specifically, M2FNet employs a multi-scale 3D-2D mixed CNN design to extract both the spatial-spectral features of HSI and the depth-based elevation features of LiDAR data. Subsequently, the extracted features are fed into a novel encoder comprising a feature enhancement module, designed with mathematical morphology and a dilated convolutional module derived from the self-attention of the conventional Transformer encoder (DConvformer), which plays a crucial role in integrating multi-source information within the network. The well-designed architecture enables the network to acquire multi-scale depth and high-order features, significantly reducing the number of training parameters. Comparative experimental results and ablation studies demonstrate that M2FNet outperforms other advanced methods. The source code is publicly available at https://github.com/cupid6868/M2FNet.git.