Remote sensing image super-resolution guided by multi-level supervision paradigm
Super-resolution improves the spatial resolution of remote sensing images,providing detailed information for various satellite applications.However,existing methods often generate pseudo-detail and lose true detail in reconstructed images due to insufficient supervision images.To address this issue,a progressive super-resolution method based on multilevel supervision structure(MSSR)was proposed.First,the MSSR introduced ground truth images as guides,which reduced the loss of true detail and mitigated the appearance of pseudo-detail in an output image.The MSSR network consists of several basic super-resolution components(BSRCs)and multilevel supervision.The overall super-resolution scale factor of the MSSR network can be set flexibly.The BSRCs can be increased or decreased,similar to building blocks,and BSRCs decrease with decreasing overall super-resolution scale factor and increase with increasing overall super-resolution scale factor.The scale factor of each BSRC is determined by the overall scale factor and the number of BSRCs.Second,a scale-factor-adjustable and lightweight basic super-resolution component was designed to enable the construction of multilevel supervision networks with different number of BSRCs and different scale factors,such as building blocks.The BSRC consists of a multiscale feature extraction module,a global feature extraction module and an image reconstruction module.Given that the scale factor of each BSRC should have a degree of flexibility,the network structure of each BSRC is the same except for the image reconstruction module.This approach shortens the overall training time of the network.Finally,a method of dividing super-resolution overall scale factor was proposed,and the effects of different number and different scale factors of BSRCs on the performance of multilevel supervision network were explored.For the super-resolution process with a certain scale factor,we need a method to divide the overall scale factor into each BSRC.The number of supervision increases with the number of BSRCs,and the super-resolution ill-posedness is reduced.In addition,the total number of network layers and the number of computations increase.We determined the optimal number of BSRCs and their respective super-resolution scale factors by comparing the super-resolution effects of multiple BSRC combinations.Additionally,a new remote sensing dataset containing worldwide scenes was constructed for the super-resolution task in this paper.To adequately train and test the proposed super-resolution method and existing methods,we used our datasets and two existing super-resolution datasets:the UCMerced and AID datasets.We compared our method with the state-of-the-art methods:VDSR,SRGAN,RDN,RCAN,DRN,and TransENet.The experiment results on three datasets demonstrated that our MSSR network outperformed these methods.The progressive network and multilevel supervision structure can effectively suppress super-resolution ill-posedness and reduce pseudo-detail and detail loss in super-resolution results.By further analyzing the experimental results,we found that multilevel supervision has greater performance gains in super-resolution tasks with a larger scale factor than other methods.We speculated that the multilevel supervision network exhibit improved performance in super-resolution tasks at 4× magnification.In future research,we will explore multilevel supervision networks in super-resolution tasks at large magnifications(i.e.,5× and 8×).
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