Road Extraction Convolutional Neural Network with Embedded Attention Mechanism for Remote Sensing Imagery

被引:17
作者
Shao, Shiwei [1 ,2 ]
Xiao, Lixia [3 ,4 ]
Lin, Liupeng [5 ]
Ren, Chang [6 ]
Tian, Jing [5 ]
机构
[1] Cent China Normal Univ, Natl Res Ctr Cultural Ind, Wuhan 430056, Peoples R China
[2] Zhongzhi Software Technol Co Ltd, Wuhan 430013, Peoples R China
[3] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
[4] Wuhan Nat Resources & Planning Informat Ctr, Wuhan 430014, Peoples R China
[5] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China
[6] Civil Aviat Flight Univ China, Coll Air Traff Management, Guanghan 618307, Peoples R China
关键词
road extraction; U-Net; attention mechanism; residual densely connected blocks; dilated convolution; SEMANTIC SEGMENTATION; CLASSIFICATION;
D O I
10.3390/rs14092061
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Roads are closely related to people's lives, and road network extraction has become one of the most important remote sensing tasks. This study aimed to propose a road extraction network with an embedded attention mechanism to solve the problem of automatic extraction of road networks from a large number of remote sensing images. Channel attention mechanism and spatial attention mechanism were introduced to enhance the use of spectral information and spatial information based on the U-Net framework. Moreover, residual densely connected blocks were introduced to enhance feature reuse and information flow transfer, and a residual dilated convolution module was introduced to extract road network information at different scales. The experimental results showed that the method proposed in this study outperformed the compared algorithms in overall accuracy. This method had fewer false detections, and the extracted roads were closer to ground truth. Ablation experiments showed that the proposed modules could effectively improve road extraction accuracy.
引用
收藏
页数:18
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