Road Extraction Method Combining Convolutional Neural Network and Tensor Voting

2020 ◽  
Vol 57 (20) ◽  
pp. 201019
Author(s):  
李天琪 Li Tianqi ◽  
谭海 Tan Hai ◽  
戴激光 Dai Jiguang ◽  
杜阳 Du Yang ◽  
王杨 Wang Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extraction Method ◽  
Tensor Voting ◽  
Road Extraction

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network

2019 ◽  
Vol 11 (5) ◽  
pp. 552 ◽  
Author(s):  
Lin Gao ◽  
Weidong Song ◽  
Jiguang Dai ◽  
Yang Chen
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Transportation Systems ◽  
Network Architectures ◽  
Tensor Voting ◽  
Road Extraction ◽  
Complex Scene ◽  
Effective Performance ◽  
Optical Satellite Images

Road extraction is one of the most significant tasks for modern transportation systems. This task is normally difficult due to complex backgrounds such as rural roads that have heterogeneous appearances with large intraclass and low interclass variations and urban roads that are covered by vehicles, pedestrians and the shadows of surrounding trees or buildings. In this paper, we propose a novel method for extracting roads from optical satellite images using a refined deep residual convolutional neural network (RDRCNN) with a postprocessing stage. RDRCNN consists of a residual connected unit (RCU) and a dilated perception unit (DPU). The RDRCNN structure is symmetric to generate the outputs of the same size. A math morphology and a tensor voting algorithm are used to improve RDRCNN performance during postprocessing. Experiments are conducted on two datasets of high-resolution images to demonstrate the performance of the proposed network architectures, and the results of the proposed architectures are compared with those of other network architectures. The results demonstrate the effective performance of the proposed method for extracting roads from a complex scene.

scholarly journals A new road extraction method using Sentinel-1 SAR images based on the deep fully convolutional neural network

2019 ◽  
Vol 52 (1) ◽  
pp. 572-582 ◽  
Author(s):  
Qianqian Zhang ◽  
Qingling Kong ◽  
Chao Zhang ◽  
Shucheng You ◽  
Hai Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extraction Method ◽  
Road Extraction ◽  
Sar Images

scholarly journals Extraction of Guardrails from MMS Data Using Convolutional Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 258-267
Author(s):  
Hiroki Matsumoto ◽  
◽  
Yuma Mori ◽  
Hiroshi Masuda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asset Management ◽  
Extraction Method ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Method Point ◽  
Mobile Mapping Systems ◽  
Capture Point ◽  
Mapping Systems

Mobile mapping systems can capture point clouds and digital images of roadside objects. Such data are useful for maintenance, asset management, and 3D map creation. In this paper, we discuss methods for extracting guardrails that separate roadways and walkways. Since there are various shape patterns for guardrails in Japan, flexible methods are required for extracting them. We propose a new extraction method based on point processing and a convolutional neural network (CNN). In our method, point clouds and images are segmented into small fragments, and their features are extracted using CNNs for images and point clouds. Then, features from images and point clouds are combined and investigated using whether they are guardrails or not. Based on our experiments, our method could extract guardrails from point clouds with a high success rate.

Convolutional neural network for road extraction

2017 ◽  
Author(s):  
Baoyu Xiong ◽  
Junping Li ◽  
Yazhou Ding ◽  
Fajie Feng ◽  
Weihong Cui
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Road Extraction
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