TY - GEN
T1 - CoA-DLinkNet
T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
AU - Li, Linghan
AU - Chen, Heliu
AU - He, Renjie
AU - Dai, Yuchao
AU - He, Mingyi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Extracting roads from high-resolution remote sensing image data presents a challenging task in the field of remote sensing image processing, which is of great significance for urban planning, vehicle navigation, and geographic information system updates. In 2018, the champion of the DeepGlobe Road Extraction Challenge proposed a creative solution named as D-LinkNet, which employed a dilated convolution cascade module to expanded the network receptive field and achieved impressive results with a concise network structure. However, D-LinkNet still suffers from low connectivity caused by road breaks. Therefore, based on D-LinkNet, we have made three improvements in this paper: 1) incorporating a strip pooling module to capture long-range anisotropic contextual information, 2) employing a parallel upsampling structure in the decoder to supervise the shallow layers, and 3) introducing a connectivity branch with a connectivity attention module to model local connectivity of roads. We have named the upgraded network as CoA-DLinkNet. Our experimental results have demonstrated that the proposed network significantly improves the prediction accuracy and connectivity of the extracted road.
AB - Extracting roads from high-resolution remote sensing image data presents a challenging task in the field of remote sensing image processing, which is of great significance for urban planning, vehicle navigation, and geographic information system updates. In 2018, the champion of the DeepGlobe Road Extraction Challenge proposed a creative solution named as D-LinkNet, which employed a dilated convolution cascade module to expanded the network receptive field and achieved impressive results with a concise network structure. However, D-LinkNet still suffers from low connectivity caused by road breaks. Therefore, based on D-LinkNet, we have made three improvements in this paper: 1) incorporating a strip pooling module to capture long-range anisotropic contextual information, 2) employing a parallel upsampling structure in the decoder to supervise the shallow layers, and 3) introducing a connectivity branch with a connectivity attention module to model local connectivity of roads. We have named the upgraded network as CoA-DLinkNet. Our experimental results have demonstrated that the proposed network significantly improves the prediction accuracy and connectivity of the extracted road.
UR - http://www.scopus.com/inward/record.url?scp=85180004140&partnerID=8YFLogxK
U2 - 10.1109/APSIPAASC58517.2023.10317237
DO - 10.1109/APSIPAASC58517.2023.10317237
M3 - 会议稿件
AN - SCOPUS:85180004140
T3 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
SP - 443
EP - 449
BT - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 October 2023 through 3 November 2023
ER -