  The classification of airborne laser scanning (ALS) point clouds is a
critical task of remote sensing and photogrammetry fields. Although recent deep
learning-based methods have achieved satisfactory performance, they have
ignored the unicity of the receptive field, which makes the ALS point cloud
classification remain challenging for the distinguishment of the areas with
complex structures and extreme scale variations. In this article, for the
objective of configuring multi-receptive field features, we propose a novel
receptive field fusion-and-stratification network (RFFS-Net). With a novel
dilated graph convolution (DGConv) and its extension annular dilated
convolution (ADConv) as basic building blocks, the receptive field fusion
process is implemented with the dilated and annular graph fusion (DAGFusion)
module, which obtains multi-receptive field feature representation through
capturing dilated and annular graphs with various receptive regions. The
stratification of the receptive fields with point sets of different resolutions
as the calculation bases is performed with Multi-level Decoders nested in
RFFS-Net and driven by the multi-level receptive field aggregation loss
(MRFALoss) to drive the network to learn in the direction of the supervision
labels with different resolutions. With receptive field
fusion-and-stratification, RFFS-Net is more adaptable to the classification of
regions with complex structures and extreme scale variations in large-scale ALS
point clouds. Evaluated on the ISPRS Vaihingen 3D dataset, our RFFS-Net
significantly outperforms the baseline approach by 5.3% on mF1 and 5.4% on
mIoU, accomplishing an overall accuracy of 82.1%, an mF1 of 71.6%, and an mIoU
of 58.2%. Furthermore, experiments on the LASDU dataset and the 2019 IEEE-GRSS
Data Fusion Contest dataset show that RFFS-Net achieves a new state-of-the-art
classification performance.
