.png){kind=icon}
返回
Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2019.2926397.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Road detection and centerline extraction from very high-resolution (VHR) remote sensing imagery are of great significance in various practical applications. Road detection and centerline extraction operations depend on each other, to a certain extent. The road detection constrains the appearance of the centerline, and the centerline enhances the linear features of the road detection. However, most of the previous works have addressed these two tasks separately and have not considered the symbiotic relationship between them, making it difficult to obtain smooth and complete roads. In this paper, a novel multi-scale and multi-task deep learning framework for automatic road extraction (MSMT-RE) is proposed to build the relationship between them and simultaneously complete the road detection and centerline extraction tasks. U-Net is selected as the basic network for multi-task learning due to its strong ability to preserve spatial details. Multi-scale feature integration is also applied in the framework to increase the robustness of the feature extraction. Meanwhile, an adaptive loss function is introduced to solve the problems of roads taking up a small percentage of the training samples, and the fact that the positive samples of the two tasks are unbalanced. Finally, experiments were conducted on two public road data sets and two large images from Google Earth, and the proposed framework was compared with other state-of-the-art deep learning-based road extraction methods, both quantitatively and qualitatively. The proposed approach outperformed all the compared methods, confirming its advantages in automatic road extraction.
Keyword:
Roads
Feature extraction
Remote sensing
Task analysis
Deep learning
Training
Labeling
Adaptive loss function
centerline extraction
deep learning framework
multi-scale and multi-task
remote sensing
road detection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Retinopathy of Prematurity in Neonates and its Risk Factors: A Seven Year Study in Northern Iran
OAAI
OAAI分享收藏Role of anthracycline and comprehensive geriatric assessment for elderly patients with diffuse large B-cell lymphomaBlood
OAAI
BloodIF0
OAAI分享收藏
查看更多内容