  In this work, we propose a novel neural network focusing on semantic labeling
of ALS point clouds, which investigates the importance of long-range spatial
and channel-wise relations and is termed as global relation-aware attentional
network (GraNet). GraNet first learns local geometric description and local
dependencies using a local spatial discrepancy attention convolution module
(LoSDA). In LoSDA, the orientation information, spatial distribution, and
elevation differences are fully considered by stacking several local spatial
geometric learning modules and the local dependencies are embedded by using an
attention pooling module. Then, a global relation-aware attention module (GRA),
consisting of a spatial relation-aware attention module (SRA) and a channel
relation aware attention module (CRA), are investigated to further learn the
global spatial and channel-wise relationship between any spatial positions and
feature vectors. The aforementioned two important modules are embedded in the
multi-scale network architecture to further consider scale changes in large
urban areas. We conducted comprehensive experiments on two ALS point cloud
datasets to evaluate the performance of our proposed framework. The results
show that our method can achieve higher classification accuracy compared with
other commonly used advanced classification methods. The overall accuracy (OA)
of our method on the ISPRS benchmark dataset can be improved to 84.5% to
classify nine semantic classes, with an average F1 measure (AvgF1) of 73.5%. In
detail, we have following F1 values for each object class: powerlines: 66.3%,
low vegetation: 82.8%, impervious surface: 91.8%, car: 80.7%, fence: 51.2%,
roof: 94.6%, facades: 62.1%, shrub: 49.9%, trees: 82.1%. Besides, experiments
were conducted using a new ALS point cloud dataset covering highly dense urban
areas.
