计算机科学
遥感
图像分辨率
卷积神经网络
高分辨率
变压器
人工智能
超分辨率
计算机视觉
图像(数学)
地理
工程类
电压
电气工程
作者
Chongjun Ye,Lingyu Yan,Yucheng Zhang,Jun Zhan,Jie Yang,Junfang Wang
标识
DOI:10.1109/idaacs53288.2021.9660904
摘要
This paper proposes a Transformers-based super-resolution method for remote sensing images. Firstly, a remote sensing image super-resolution network based on convolutional neural network and Transformer module is constructed; then the training data is used to train the remote sensing image super-resolution network and the optimized network parameters are obtained; finally, the trained remote sensing image super-resolution network is used to super-resolve low-resolution remote sensing images to obtain high-resolution remote sensing images. Experiments are conducted on the public remote sensing dataset (UC Mercedes) and compared with several traditional super-resolution algorithms. The results show that the present algorithm is highly automated and has improved in both accuracy and efficiency.
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