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Acta Optica Sinica, Volume. 40, Issue 3, 0328001(2020)

Improved Moving Surface Algorithm Based on Confidence Interval Estimation Theory

Chengbin Xing, Xingsheng Deng*, and Kang Xu
Author Affiliations
  • Department of Surveying and Mapping Engineering, School of Traffic & Transportation Engineering, Changsha University of Science and Technology, Changsha, Hunan 410004, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (24)
    Equations (0)
    References (14)
    Cited By (6)
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    Paper Information
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    Figures & Tables(24)
    Creating map area based on coordinate extreme values

    Fig. 1. Creating map area based on coordinate extreme values

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    Established grid index diagram

    Fig. 2. Established grid index diagram

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    Distribution of seed points in small grid

    Fig. 3. Distribution of seed points in small grid

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    Quadric surface fitted by seed points in small grid

    Fig. 4. Quadric surface fitted by seed points in small grid

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    Index grids

    Fig. 5. Index grids

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    Small grid in large grid

    Fig. 6. Small grid in large grid

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    Distances between other points and plane ΔACD

    Fig. 7. Distances between other points and plane ΔACD

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    Distances between other points and plane ΔADE

    Fig. 8. Distances between other points and plane ΔADE

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    Flow chart of improved filtering algorithm

    Fig. 9. Flow chart of improved filtering algorithm

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    Distribution of feature points and ground points before filtering

    Fig. 10. Distribution of feature points and ground points before filtering

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    Ground point distribution after filtering

    Fig. 11. Ground point distribution after filtering

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    Ground point distribution of elevation difference between real terrain and fitted terrain

    Fig. 12. Ground point distribution of elevation difference between real terrain and fitted terrain

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    Distribution of feature points and ground points under threshold conditions

    Fig. 13. Distribution of feature points and ground points under threshold conditions

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    Distribution of feature points and ground points of Sample41 before filtering

    Fig. 14. Distribution of feature points and ground points of Sample41 before filtering

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    Ground point distribution of Sample41 after filtering

    Fig. 15. Ground point distribution of Sample41 after filtering

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    Distribution of elevation difference between real terrain and fitted terrain of Sample41

    Fig. 16. Distribution of elevation difference between real terrain and fitted terrain of Sample41

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    Distribution of Sample41 feature points and ground points under threshold condition

    Fig. 17. Distribution of Sample41 feature points and ground points under threshold condition

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    Ground point distribution after filtering by improved moving surface algorithm

    Fig. 18. Ground point distribution after filtering by improved moving surface algorithm

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    Distribution of ground points after filtering by classical moving surface algorithm

    Fig. 19. Distribution of ground points after filtering by classical moving surface algorithm

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    • Table 1. Test dataset and sample attributes released by ISPRS

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      Table 1. Test dataset and sample attributes released by ISPRS

      Test dataSampleTopographical feature
      Site1Sample11Vegetation and buildings on steep slopes
      Sample12Buildings and small objects on the ground
      Sample21Narrow bridge
      Site2Sample22Bridge
      Sample23Complex buildings, discontinuous terrain
      Sample24Steep slopes and vegetation
      Site3Sample31There is a low value noise point
      Site4Sample41Discontinuous terrain
      Sample42High frequency terrain relief
      Sample51Vegetation on the slope
      Site5Sample52Steep slope
      Sample53Discontinuous terrain
      Sample54Village
      Site6Sample61Discontinuous steep slope
      Site7Sample71Bridge
      Site8No sampleIntermittent terrain, ridge

    • Table 2. Definition of filter error

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      Table 2. Definition of filter error

      SampleFiltered dataSample data point
      Ground pointFeature point
      Ground pointabe=a+b
      Feature pointcdf=c+d
      Filtered pointg=a+ch=b+dm=a+b+c+d

    • Table 3. Number of ground and non-ground points in the sample survey area and three types of error ratio

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      Table 3. Number of ground and non-ground points in the sample survey area and three types of error ratio

      TestdataSample11Sample23Sample41Sample51Sample53
      Sample attributeVegetation and buildings on steep slopesComplex buildingAggregate low value pointsLow vegetation, steep slope, ridgeIntermittent terrain
      Number of sample points3801025095112311784534378
      Filtered feature point185761310949081287828324
      Filtered ground point1943411986632349676054
      Type I error /%17.8016.3713.439.7314.50
      Type II error /%4.128.7210.307.318.52
      Total error /%11.909.0812.358.4114.20

    • Table 4. Comparison of accuracies of 4 filter algorithms%

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      Table 4. Comparison of accuracies of 4 filter algorithms%

      Data sampleType of errorPTD algorithmMorphological algorithmMoving surface algorithmOur algorithm
      Type I error15.9621.9721.5217.80
      Sample11Type II error3.653.165.954.12
      Total error10.7617.3614.8711.90
      Type I error12.0813.3018.3916.37
      Sample23Type II error3.8114.909.028.72
      Total error8.2214.1014.729.08
      Type I error8.5812.5312.2314.50
      Sample53Type II error16.7614.2342.778.52
      Total error8.9112.6017.7114.20
      Type I error7.1522.431.841.87
      Sample61Type II error0.170.946.795.43
      Total error6.9121.682.011.99

    • Table 5. Statistics of three types of error of improved algorithm and classical algorithm for Sample51%

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      Table 5. Statistics of three types of error of improved algorithm and classical algorithm for Sample51%

      Type of errorImproved moving surface algorithmClassical moving surface algorithm
      Type I error9.7311.23
      Type II error7.319.34
      Total error8.4110.27
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    Chengbin Xing, Xingsheng Deng, Kang Xu. Improved Moving Surface Algorithm Based on Confidence Interval Estimation Theory[J]. Acta Optica Sinica, 2020, 40(3): 0328001

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Jul. 26, 2019

    Accepted: Oct. 8, 2019

    Published Online: Feb. 10, 2020

    The Author Email: Xingsheng Deng (whudxs@163.com)

    DOI:10.3788/AOS202040.0328001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology