предыдущий|следующий

Remote sensing image super-resolution guided by multi-level supervision paradigm

  • role： First author第一作者
  • Affiliation：

    School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

  • Email：limingkai@bit.edu.cn
  • Introduction：李明锴，研究方向为深度学习、空天智能计算。E-mail： limingkai@bit.edu.cn
LI Mingkai,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China

  • Email：qizhi@bit.edu.cn
  • Introduction：徐其志，研究方向为多源遥感数据融合、目标检测跟踪、在轨信息智能处理。E-mail： qizhi@bit.edu.cn
XU Qizhi*

реферат

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×).

ключеви́че слова́

remote sensing image;deep learning;multi-level supervision;super-resolution;multi-scale feature extration;progressive network;parameter sharing;transfer learning

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