Journal
REMOTE SENSING
Volume 11, Issue 23, Pages -Publisher
MDPI
DOI: 10.3390/rs11232857
Keywords
super-resolution; remote sensing; attention mechanism; transfer learning
Categories
Funding
- National Natural Science Foundation of China [41722108, 91638201]
- National Key Research and Development Program of China [2016YFB0501501]
- Fundamental Research Funds for the Central Universities [3072019CF0801]
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Image super-resolution (SR) reconstruction plays a key role in coping with the increasing demand on remote sensing imaging applications with high spatial resolution requirements. Though many SR methods have been proposed over the last few years, further research is needed to improve SR processes with regard to the complex spatial distribution of the remote sensing images and the diverse spatial scales of ground objects. In this paper, a novel multi-perception attention network (MPSR) is developed with performance exceeding those of many existing state-of-the-art models. By incorporating the proposed enhanced residual block (ERB) and residual channel attention group (RCAG), MPSR can super-resolve low-resolution remote sensing images via multi-perception learning and multi-level information adaptive weighted fusion. Moreover, a pre-train and transfer learning strategy is introduced, which improved the SR performance and stabilized the training procedure. Experimental comparisons are conducted using 13 state-of-the-art methods over a remote sensing dataset and benchmark natural image sets. The proposed model proved its excellence in both objective criterion and subjective perspective.
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