DAEA-Net: Dual Attention and Elevation-Aware Networks for Airborne LiDAR Point Cloud Semantic Segmentation

Semantic segmentation of airborne laser scanning (ALS) point clouds remains a challenging task due to the complexity and diversity of 3-D scenes in the real world. Currently, most deep learning-based airborne LiDAR point cloud segmentation methods prioritize designing local feature extraction operators while overlooking the long-range dependencies among neighborhoods and the inherently diverse properties of point cloud data. To address these issues, this article introduces a dual-attention and elevation-aware airborne LiDAR point cloud semantic segmentation network (DAEA-Net) built upon an encoding-decoding architecture. First, we develop a cross multiple anti-affine attention (CMAAA) module that effectively captures global contextual information across different neighborhoods through interactive learning of multiple features. Second, we introduce an elevation awareness (EA) module that uses normal vectors to establish a geometric similarity discriminant for each neighboring point. It incorporates an autoencoder architecture to fuse elevation information, enhancing the horizontal structural dissimilarity between objects of similar height while enriching the representation of elevation data. Additionally, to compensate for the potential information loss in the encoding-decoding hierarchical structure, we design a lightweight U-global attention (UGA) module to link decoding and encoding hierarchical levels. It merges features of different resolutions and levels during downsampling and upsampling through pooling while utilizing the self-attention mechanism to enhance the network's global expression capability. The proposed DAEA-Net enhances ALS semantic segmentation performance by enabling interactive learning of multiple features and effectively representing elevation information. Extensive experiments conducted on two datasets demonstrate that our method delivers superior semantic segmentation performance compared to several existing advanced techniques.