HA-GCN: an ALS point cloud classification method based on height-aware graph convolution network

Pei Wen; Yinglei Cheng; Peng Wang; Mingjun Zhao; Bixiu Zhang

doi:10.1117/12.2623390

16 February 2022 HA-GCN: an ALS point cloud classification method based on height-aware graph convolution network

Pei Wen, Yinglei Cheng, Peng Wang, Mingjun Zhao, Bixiu Zhang

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831C (2022) https://doi.org/10.1117/12.2623390
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

In this paper, we propose a Height-Aware Graph Convolution Network (HA-GCN) to solve the challenging problem of Airborne laser scanning (ALS) point cloud classification. For samples with uneven distribution and large differences in scale, classification using local features is unstable and easily affected by noise. Therefore, we use a multi-layer stacked Edge Convolution (EdgeConv) operators to extract local and global information at the same time. In addition, in view of the characteristics of the height distribution of airborne LiDAR point cloud, we introduce height attention weights as a supplement to feature extraction. First, the original point cloud is divided into sub-blocks and sampled to a fixed number of points. Then, the EdgeConv operator is used to extract local-global features. At the same time, the Height-Aware (HA) module is used to generate attention weights. Finally, the height attention weights are applied to the feature extraction network and the classification is completed after post-processing. The experimental results on the Vaihingen dataset show that the proposed method achieves the effect of the state-of-the-art methods in overall accuracy, as well as impressive results in single-category classification accuracy.

Citation Download Citation

Pei Wen, Yinglei Cheng, Peng Wang, Mingjun Zhao, and Bixiu Zhang "HA-GCN: an ALS point cloud classification method based on height-aware graph convolution network", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831C (16 February 2022); https://doi.org/10.1117/12.2623390

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KEYWORDS

Clouds

Convolution

Feature extraction

LIDAR

Classification systems

Buildings

Data modeling

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