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用于遥感图像超分辨率重建的残差对偶回归网络

尚晓梅, 李佳田, 吕少云, 杨汝春, 杨超. 2022. 用于遥感图像超分辨率重建的残差对偶回归网络. 自然资源遥感, 34(2): 112-120. doi: 10.6046/zrzyyg.2021208
引用本文: 尚晓梅, 李佳田, 吕少云, 杨汝春, 杨超. 2022. 用于遥感图像超分辨率重建的残差对偶回归网络. 自然资源遥感, 34(2): 112-120. doi: 10.6046/zrzyyg.2021208
SHANG Xiaomei, LI Jiatian, LYU Shaoyun, YANG Ruchun, YANG Chao. 2022. Residual dual regression network for super-resolution reconstruction of remote sensing images. Remote Sensing for Natural Resources, 34(2): 112-120. doi: 10.6046/zrzyyg.2021208
Citation: SHANG Xiaomei, LI Jiatian, LYU Shaoyun, YANG Ruchun, YANG Chao. 2022. Residual dual regression network for super-resolution reconstruction of remote sensing images. Remote Sensing for Natural Resources, 34(2): 112-120. doi: 10.6046/zrzyyg.2021208

用于遥感图像超分辨率重建的残差对偶回归网络

  • 基金项目:

    国家自然科学基金项目”城市居住区识别的Voronoi邻域方法与初步实践”(41561082)

详细信息
    作者简介: 尚晓梅(1997-),女,硕士研究生,主要研究方向为摄影测量与遥感。Email: sxm.320@qq.com
  • 中图分类号: TP79

Residual dual regression network for super-resolution reconstruction of remote sensing images

  • 使用人工模拟的高-低分辨率图像对易导致在对真实遥感图像超分辨率重建时模型泛化能力差,针对此问题,结合残差通道注意力网络(residual channel attention network,RCAN)的二次残差(residual in residual,RIR)模块,改进对偶回归网络(dual regression networks,DRN),提出了残差对偶回归网络(residual dual regression network,RDRN)。选取LandCover.ai和DIOR航空图像数据集的10 000张512像素x512像素图像构成样本数据集,用于训练和测试网络,并将重建结果与现有其他超分辨率网络模型的重建结果对比评价。实验结果表明,RDRN在重建质量和模型参数量方面均表现优异,能够在较低模型复杂度的情况下实现较好的超分重建效果,且对不同低分辨率遥感图像具有较好的泛化能力。
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出版历程
收稿日期:  2021-06-30
刊出日期:  2022-06-20

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