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