期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 14, 期 8, 页码 1243-1247出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2704122
关键词
Convolutional neural networks (CNNs); local-global combined network (LGCNet); remote sensing images; super-resolution
类别
资金
- National Natural Science Foundation of China [61671037]
- Beijing Natural Science Foundation [4152031]
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University [BUAA-VR-16ZZ-03]
- Fundamental Research Funds for the Central Universities [YWF-16-BJ-J-30]
Super-resolution is an image processing technology that recovers a high-resolution image from a single or sequential low-resolution images. Recently deep convolutional neural networks (CNNs) have made a huge breakthrough in many tasks including super-resolution. In this letter, we propose a new singleimage super-resolution algorithm named local-global combined networks (LGCNet) for remote sensing images based on the deep CNNs. Our LGCNet is elaborately designed with its multifork structure to learn multilevel representations of remote sensing images including both local details and global environmental priors. Experimental results on a public remote sensing data set (UC Merced) demonstrate an overall improvement of both accuracy and visual performance over several state-of-the-art algorithms.
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