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Open AccessArticle

Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2857;
Received: 12 October 2019 / Revised: 28 November 2019 / Accepted: 29 November 2019 / Published: 1 December 2019
(This article belongs to the Special Issue Image Super-Resolution in Remote Sensing)
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. View Full-Text
Keywords: super-resolution; remote sensing; attention mechanism; transfer learning super-resolution; remote sensing; attention mechanism; transfer learning
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MDPI and ACS Style

Dong, X.; Xi, Z.; Sun, X.; Gao, L. Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution. Remote Sens. 2019, 11, 2857.

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