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SCIENTIA SINICA Informationis, Volume 48 , Issue 8 : 1022-1034(2018) https://doi.org/10.1360/N112017-00208

Object detection models of remote sensing images using deep neural networks with weakly supervised training method

More info
  • ReceivedJan 2, 2018
  • AcceptedJan 19, 2018
  • PublishedAug 8, 2018

Abstract


Funded by

国家自然科学基金(41471280,61701290,61701289)


References

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  • Figure 1

    (Color online) The object detection framework of remote sensing images with machine learning and deep learning

  • Figure 2

    (Color online) The framework of the WS-DNN object detection model

  • Figure 3

    (Color online) The image samples of training set and validating set in (a) satellite aircrafts dataset andprotectłinebreak (b) aircrafts dataset [6]

  • Figure 4

    (Color online) The image samples of testing set in (a) satellite aircrafts dataset and (b) aircrafts dataset [6]

  • Figure 5

    (Color online) The training process of CNN and FCN model

  • Figure 6

    (Color online) The comparison of processing results of different FCN models. (a) Original image; (b) Pascal-FCN-32 model; (c) Pascal-FCN-16 model; (d) Pascal-FCN-8 model

  • Figure 7

    (Color online) The comparison of BBFT and NMS algorithm. (a) Image region 1; (b) NMS and (c) BBFT results of image region 1; (d) image region 2; (e) NMS and (f) BBFT results of image region 2

  • Figure 8

    (Color online) Some detected results of the WS-DNN model. Detections of image (a) 1, (b) 2, (c) 3

  • Table 1   The comparison of different proposal extracting algorithms
    Algorithm FPR-SAD (%) MR-SAD (%) Runtime-SAD (s) FPR-AD (%) MR-AD (%) Runtime-AD (s)
    Sliding window 91.21 0 0.59 95.29 0 1.01
    Selective search [11] 90.27 0.03 27.96 87.85 0 24.06
    FCN 78.16 0 7.73 84.08 0 11.15
  • Table 2   The experimental results of the WS-DNN model on satellite aircrafts dataset
    Evaluating method Results (%)
    FAR 7.69
    MR 3.42
    PR 93.22
  • Table 3   The comparison of different object detection methods on aircrafts dataset
    Given MR (%) FAR of BING-CNN [6] (%) PR of BING-CNN [6] (%) FAR of WS-DNN (%) PR of WS-DNN (%)
    25 7.28 90.50 4.65 94.12
    20 9.27 90.00 8.74 90.53
    15 17.66 84.00 10.26 89.33