TY - GEN
T1 - A Novel Impervious Surface Extraction Method Based on Transformer
AU - Zhang, Wenjing
AU - Qiu, Dehui
AU - Wan, Xiaohua
AU - Zhang, Fa
AU - Yuan, Huimei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The amount of impervious surface is an important indicator to measure the degree of urbanization and the urban ecological environment. However, the objects in the low-density impervious surface areas are small and scattered, which are easily confused with the background. Therefore, the extraction of the small and scattered impervious surfaces is still challenging. In this study, we propose a dual-branch network combing transformer and CNN with attention mechanism. In this model, transformer branch is first used to extract impervious surface to capture long-distance and large-scale dependencies. In addition, another UNet branch embedded the coordinate attention mechanism can capture detailed information and meanwhile reduce information redundancy. Experiments show that our proposed method performs better than the traditional CNN methods.
AB - The amount of impervious surface is an important indicator to measure the degree of urbanization and the urban ecological environment. However, the objects in the low-density impervious surface areas are small and scattered, which are easily confused with the background. Therefore, the extraction of the small and scattered impervious surfaces is still challenging. In this study, we propose a dual-branch network combing transformer and CNN with attention mechanism. In this model, transformer branch is first used to extract impervious surface to capture long-distance and large-scale dependencies. In addition, another UNet branch embedded the coordinate attention mechanism can capture detailed information and meanwhile reduce information redundancy. Experiments show that our proposed method performs better than the traditional CNN methods.
KW - Coordinate attention
KW - Impervious surface extraction
KW - Semantic segmentation
KW - Transformer
UR - http://www.scopus.com/inward/record.url?scp=85178363973&partnerID=8YFLogxK
U2 - 10.1109/IGARSS52108.2023.10282343
DO - 10.1109/IGARSS52108.2023.10282343
M3 - Conference contribution
AN - SCOPUS:85178363973
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6851
EP - 6854
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -
