TY - GEN
T1 - Transformer and CNN Hybrid Network for Super-Resolution Semantic Segmentation of Remote Sensing Imagery
AU - Liu, Yutong
AU - Gao, Kun
AU - Wang, Hong
AU - Wang, Junwei
AU - Zhang, Xiaodian
AU - Wang, Pengyu
AU - Li, Shuzhong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Super-resolution semantic segmentation (SRSS) based on Convolutional neural network (CNN) cannot establish long-range dependencies due to limited receptive field, which limits the SRSS to obtain accurate high-resolution (HR) segmentation results from the low-resolution (LR) input images. In this paper, we design a Transformer and CNN hybrid SRSS network that consists of two branches: Transformer and CNN hybrid SRSS branch and super-resolution guided branch. In the Transformer and CNN hybrid SRSS branch, Transformer extracts global context information from the feature map of the CNN, while skip connection is used to retain the local context information extracted from the CNN and combines both features to further improve the segmentation performance. In addition, the super-resolution guided branch is designed to supplement rich structure information and guide the semantic segmentation (SS). We test the proposed method on the ISPRS Vaihingen benchmark data set, and our network is superior to other state-of-the-art methods.
AB - Super-resolution semantic segmentation (SRSS) based on Convolutional neural network (CNN) cannot establish long-range dependencies due to limited receptive field, which limits the SRSS to obtain accurate high-resolution (HR) segmentation results from the low-resolution (LR) input images. In this paper, we design a Transformer and CNN hybrid SRSS network that consists of two branches: Transformer and CNN hybrid SRSS branch and super-resolution guided branch. In the Transformer and CNN hybrid SRSS branch, Transformer extracts global context information from the feature map of the CNN, while skip connection is used to retain the local context information extracted from the CNN and combines both features to further improve the segmentation performance. In addition, the super-resolution guided branch is designed to supplement rich structure information and guide the semantic segmentation (SS). We test the proposed method on the ISPRS Vaihingen benchmark data set, and our network is superior to other state-of-the-art methods.
KW - Remote Sensing
KW - Semantic Segmentation
KW - Super-Resolution Semantic Segmentation
KW - Transformer
UR - http://www.scopus.com/inward/record.url?scp=85178330094&partnerID=8YFLogxK
U2 - 10.1109/IGARSS52108.2023.10282558
DO - 10.1109/IGARSS52108.2023.10282558
M3 - Conference contribution
AN - SCOPUS:85178330094
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6940
EP - 6943
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 -