TY - GEN
T1 - SAR Image Super-Resolution Base on Weighted Dense Connected Convolutional Network
AU - Yu, Jianwen
AU - Li, Wenchao
AU - Li, Zhongyu
AU - Wu, Junjie
AU - Yang, Haiguang
AU - Yang, Jianyu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - In this paper, a weighted dense connected convolutional network(WDCCN) is proposed for SAR image super-resolution. In the network, to enhance feature propagation and the super-resolution performance, each layer will receive the output from all the previous layers in a different weight proportion. At last, the experimental results indicate that weighted dense connected convolutional network can realize SAR image super-resolution well.
AB - In this paper, a weighted dense connected convolutional network(WDCCN) is proposed for SAR image super-resolution. In the network, to enhance feature propagation and the super-resolution performance, each layer will receive the output from all the previous layers in a different weight proportion. At last, the experimental results indicate that weighted dense connected convolutional network can realize SAR image super-resolution well.
KW - SAR
KW - convolutional network
KW - super-resolution
KW - weighted dense connection
UR - https://www.scopus.com/pages/publications/85101960512
U2 - 10.1109/IGARSS39084.2020.9324079
DO - 10.1109/IGARSS39084.2020.9324079
M3 - 会议稿件
AN - SCOPUS:85101960512
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2101
EP - 2104
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -