@Article{cmc.2020.011262,
AUTHOR = {Xu Han, Huijun Yang, Qiufeng Shen, Jiangtao Yang, Huihui Liang, Cancan Bao, Shuang Cang},
TITLE = {Automatic Terrain Debris Recognition Network Based on 3D  Remote Sensing Data},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {579--596},
URL = {http://www.techscience.com/cmc/v65n1/39584},
ISSN = {1546-2226},
ABSTRACT = {Although predecessors have made great contributions to the semantic 
segmentation of 3D indoor scenes, there still exist some challenges in the debris 
recognition of terrain data. Compared with hundreds of thousands of indoor point clouds, 
the amount of terrain point cloud is up to millions. Apart from that, terrain point cloud 
data obtained from remote sensing is measured in meters, but the indoor scene is 
measured in centimeters. In this case, the terrain debris obtained from remote sensing 
mapping only have dozens of points, which means that sufficient training information 
cannot be obtained only through the convolution of points. In this paper, we build multiattribute descriptors containing geometric information and color information to better 
describe the information in low-precision terrain debris. Therefore, our process is aimed 
at the multi-attribute descriptors of each point rather than the point. On this basis, an 
unsupervised classification algorithm is proposed to divide the point cloud into several 
terrain areas, and regard each area as a graph vertex named super point to form the graph 
structure, thus effectively reducing the number of the terrain point cloud from millions to 
hundreds. Then we proposed a graph convolution network by employing PointNet for 
graph embedding and recurrent gated graph convolutional network for classification. Our 
experiments show that the terrain point cloud can reduce the amount of data from 
millions to hundreds through the super point graph based on multi-attribute descriptor 
and our accuracy reached 91.74% and the IoU reached 94.08%, both of which were 
significantly better than the current methods such as SEGCloud (Acc: 88.63%, IoU: 
89.29%) and PointCNN (Acc: 86.35, IoU: 87.26).},
DOI = {10.32604/cmc.2020.011262}
}