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面向目标检测的双驱自适应遥感图像超分重建方法

成科扬 荣兰 蒋森林 詹永照

成科扬, 荣兰, 蒋森林, 等 . 面向目标检测的双驱自适应遥感图像超分重建方法[J]. 北京航空航天大学学报, 2022, 48(8): 1343-1352. doi: 10.13700/j.bh.1001-5965.2021.0517
引用本文: 成科扬, 荣兰, 蒋森林, 等 . 面向目标检测的双驱自适应遥感图像超分重建方法[J]. 北京航空航天大学学报, 2022, 48(8): 1343-1352. doi: 10.13700/j.bh.1001-5965.2021.0517
CHENG Keyang, RONG Lan, JIANG Senlin, et al. Double drive adaptive super-resolution reconstruction method of remote sensing images for object detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1343-1352. doi: 10.13700/j.bh.1001-5965.2021.0517(in Chinese)
Citation: CHENG Keyang, RONG Lan, JIANG Senlin, et al. Double drive adaptive super-resolution reconstruction method of remote sensing images for object detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1343-1352. doi: 10.13700/j.bh.1001-5965.2021.0517(in Chinese)

面向目标检测的双驱自适应遥感图像超分重建方法

doi: 10.13700/j.bh.1001-5965.2021.0517
基金项目: 

国家自然科学基金 61972183

江苏省科技项目 BE2022781

镇江市“金山英才”高层次领军人才培养计划培养对象科研项目 

详细信息
    通讯作者:

    成科扬, E-mail: kycheng@ujs.edu.cn

  • 中图分类号: V221+.3;TB553

Double drive adaptive super-resolution reconstruction method of remote sensing images for object detection

Funds: 

National Natural Science Foundation of China 61972183

Jiangsu Science and Technology Project BE2022781

Zhenjiang Jinshan High-level Leading Talents Training Plan Scientific Research Project 

More Information
  • 摘要:

    有光学遥感图像超分重建方法主要是生成视觉上令人满意的图像,并未考虑后续目标检测任务的特殊性,不能有效地应用到目标检测中。基于此,提出了面向目标检测的双驱动自适应多尺度光学遥感图像超分重建方法,将超分重建网络和目标检测网络结合起来,进行联合优化。针对光学遥感图像的特点设计了自适应多尺度遥感图像超分重建网络,集成选择性内核网络和自适应特征门控单元来特征提取和融合,重建出初步遥感图像。通过提出的双驱动模块,将特征先验驱动损失和任务驱动损失传到超分重建网络中,提高目标检测的性能。在UCAS-AOD和NWPU VHR-10数据集上进行实验,并与5种主流方法进行比较,所提方法的峰值信噪比和平均准确率相较于FDSR方法分别提高了1.86 dB和3.73%。实验结果表明,所提方法和光学遥感图像目标检测结合可以取得更好的效果,综合性能更佳。

     

  • 图 1  整体网络结构

    Figure 1.  Overall network structure

    图 2  自适应多尺度特征提取块结构

    Figure 2.  Adaptive multi-scale feature extraction block structure

    图 3  自适应特征门控单元结构

    Figure 3.  Adaptive feature gating structure

    图 4  不同λ1λ2下本文方法的检测性能

    Figure 4.  Proposed method detects performance under different λ1 and λ2

    图 5  检测效果对比示例

    Figure 5.  Example of detection effect comparison

    表  1  消融实验

    Table  1.   Ablation test

    实验 PSRN/dB mAP/%
    FR-CNN(HR) 70.43
    FR-CNN(LR) 40.32
    AMNN+FR-CNN
    (无联合训练)
    27.75 55.34
    AMNN+FR-CNN+TD
    (联合训练)
    28.06 65.33
    AMNN+FR-CNN+TD+FPD
    (联合训练)
    28.79 69.13
    下载: 导出CSV

    表  2  不同方法在UCAS-AOD数据集飞机类上的实验效果比较

    Table  2.   Experimental results compared with different methods on UCAS-AOD dataset

    方法 PSNR/dB AP/% AP0.5/% AP0.75/% APS/% APM/% APL/%
    原始图像 47.6 59.2 41.1 21.5 48.5 58.7
    Bicubicu[19] 25.89 22.14 37.9 23.01 6.71 23.46 38.15
    MSRN[20] 28.15 23.45 44.5 24.78 8.83 25.67 43.63
    TDSR[10] 27.34 26.34 46.98 27.78 9.04 28.84 45.96
    AMFFN[7] 28.56 24.78 44.54 25.01 8.92 25.65 43.98
    FDSR[21] 27.56 26.33 46.78 27.66 9.15 29.03 45.97
    本文方法 28.97 44.89 55.02 37.32 20.3 38.45 57.87
    注:黑体数据表示最优结果。
    下载: 导出CSV

    表  3  不同方法在UCAS-AOD数据集上的实验效果比较

    Table  3.   Experimental results compared with different methods on UCAS-AOD dataset

    方法 PSNR/dB mAP/%
    Bicubicu[19] 25.95 48.85
    MSRN[20] 27.55 58.97
    AMFFN[7] 27.56 59.23
    TDSR[10] 26.43 62.96
    FDSR[21] 26.65 63.98
    本文方法 28.75 69.67
    注:黑体数据表示最优结果。
    下载: 导出CSV

    表  4  不同方法在NWPU VHR-10数据集上的实验效果比较

    Table  4.   Experimental results compared with different methods on NWPU VHR-10 dataset

    方法 PSNR/dB mAP/%
    Bicubicu[19] 24.86 47.56
    MSRN[20] 27.01 57.98
    AMFFN[7] 27.12 58.32
    TDSR[10] 26.82 62.97
    FDSR[21] 26.96 63.84
    本文方法 28.58 68.61
    注:黑体数据表示最优结果。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-06
  • 录用日期:  2021-09-17
  • 刊出日期:  2021-10-29

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