Open Access

ARTICLE

Automatic Terrain Debris Recognition Network Based on 3D Remote Sensing Data

Xu Han^{1, #}, Huijun Yang^{1, 4, *}, Qiufeng Shen^{1, #}, Jiangtao Yang², Huihui Liang¹, Cancan Bao¹, Shuang Cang³

1 College of Information Engineering, Northwest A&F University, Yangling, 712100, China.
2 College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, China.
3 School of Science and Technology, Nottingham Trent University, Nottingham, NG118NS, UK.
4 Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, 712100, China.

* Corresponding Author: Huijun Yang. Email: .

Computers, Materials & Continua 2020, 65(1), 579-596. https://doi.org/10.32604/cmc.2020.011262

Received 01 May 2020; Accepted 25 May 2020; Issue published 23 July 2020

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).

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Citations