兰州大学机构库
Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images
Li, Jing1; Liu, Y(刘勇)1; Zhang, Yindan1; Zhang, Yang2
2021-05
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
EISSN2220-9964
Volume10Issue:5
page numbers20
AbstractThe use of very-high-resolution images to extract urban, suburban and rural roads has important application value. However, it is still a problem to effectively extract the road area occluded by roadside tree canopy or high-rise buildings to maintain the integrity of the extracted road area, the smoothness of the sideline and the connectivity of the road network. This paper proposes an innovative Cascaded Attention DenseUNet (CADUNet) semantic segmentation model by embedding two attention modules, such as global attention and core attention modules, in the DenseUNet framework. First, a set of cascaded global attention modules are introduced to obtain the contextual information of the roadsecondly, a set of cascaded core attention modules are embedded to ensure that the road information is transmitted to the greatest extent among the dense blocks in the network, and further assist the global attention module in acquiring multi-scale road information, thereby improving the connectivity of the road network while restoring the integrity of the road area shaded by the tree canopy and high-rise buildings. Based on binary cross entropy, an adaptive loss function is proposed for network parameter tuning. Experiments on the Massachusetts road dataset and the DeepGlobe-CVPR 2018 road dataset show that this semantic segmentation model can effectively extract the road area shaded by tree canopy and improve the connectivity of the road network.
Keyworddeep learningroadDenseUNetattention modulesemantic segmentationremote sensing
PublisherMDPI
DOI10.3390/ijgi10050329
Indexed BySCOPUS ; SCIE
Language英语
WOS Research AreaComputer SciencePhysical GeographyRemote Sensing
WOS SubjectComputer Science, Information SystemsGeography, PhysicalRemote Sensing
WOS IDWOS:000654029700001
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/450963
Collection兰州大学
资源环境学院
Corresponding AuthorLiu, Yong
Affiliation
1.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China;
2.Lanzhou Univ, Supercomp Ctr, Lanzhou 730000, Peoples R China
First Author AffilicationCollege of Earth Environmental Sciences
Corresponding Author AffilicationCollege of Earth Environmental Sciences
First Signature AffilicationCollege of Earth Environmental Sciences
Recommended Citation
GB/T 7714
Li, Jing,Liu, Yong,Zhang, Yindan,et al. Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2021,10(5).
APA Li, Jing,Liu, Yong,Zhang, Yindan,&Zhang, Yang.(2021).Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,10(5).
MLA Li, Jing,et al."Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 10.5(2021).
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