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Laser & Optoelectronics Progress, Volume. 56, Issue 2, 021101(2019)

Hyperspectral Image Quality Evaluation Based on Multi-Model Fusion

Dongyu Xu1, Xiaorun Li1、*, Liaoying Zhao2, Rui Shu3, and Qijia Tang3
Author Affiliations
  • 1 College of Electrical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
  • 2 Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
  • 3 Shanghai Institute of Satellite Engineering, Shanghai 200240, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    References (22)
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    Figures & Tables(10)
    Scene images with features of cloud-only. (a) Sample A; (b) sample B; (c) sample C; (d) sample D; (e) sample E; (f) sample F

    Fig. 1. Scene images with features of cloud-only. (a) Sample A; (b) sample B; (c) sample C; (d) sample D; (e) sample E; (f) sample F

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    Scene images with features of land and sea. (a) Sample G; (b) sample H; (c) sample I; (d) sample J

    Fig. 2. Scene images with features of land and sea. (a) Sample G; (b) sample H; (c) sample I; (d) sample J

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    Scene images with features of land, sea and cloud. (a)Sample K; (b) sample L; (c) sample M

    Fig. 3. Scene images with features of land, sea and cloud. (a)Sample K; (b) sample L; (c) sample M

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    Simulation images of degraded factors. (a) Simulation image of noise; (b) simulation image of ambiguity

    Fig. 4. Simulation images of degraded factors. (a) Simulation image of noise; (b) simulation image of ambiguity

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    Flow chart of cloud content detection via hyperspectral remote sensing image

    Fig. 5. Flow chart of cloud content detection via hyperspectral remote sensing image

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    Structural diagram of GRNN network

    Fig. 6. Structural diagram of GRNN network

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    Structural diagram of multi-model fusion integrated quality evaluation model

    Fig. 7. Structural diagram of multi-model fusion integrated quality evaluation model

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    Fitting results by various regression algorithms. (a) SVR; (b) Bagging; (c) model fusion; (d) GRNN

    Fig. 8. Fitting results by various regression algorithms. (a) SVR; (b) Bagging; (c) model fusion; (d) GRNN

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    • Table 1. Details of feature scene images

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      Table 1. Details of feature scene images

      SampleImaging timeLocationType
      A2017-02-03Hong Kong, ChinaCloud
      B2016-09-06Japan IslandCloud
      C2017-04-23Caribbean SeaCloud
      D2017-02-03Hong Kong, ChinaCloud
      E2016-09-06Japan IslandCloud
      F2017-04-23Caribbean SeaCloud
      G2017-02-03Hong Kong, ChinaSea (basis), land
      H2016-09-06Japan IslandSea (basis), land
      I2017-04-23Caribbean SeaSea (basis), land
      J2017-02-03Hong Kong, ChinaSea, land (basis)
      K2016-09-06Japan IslandSea, land, cloud
      L2017-04-23Caribbean SeaSea, land, cloud
      M2017-02-03Hong Kong, ChinaSea, land, cloud

    • Table 2. Comparison of results by regression algorithms

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      Table 2. Comparison of results by regression algorithms

      MethodTrainingtime /sTraining setTesting set
      Meansquare errorFittingindicator R2Classificationaccuracy /%Meansquare errorFittingindicator R2Classificationaccuracy /%
      GRNN14.3560.02070.993698.1570.58960.867995.333
      SVR3.5830.31480.940296.8130.78550.820994.667
      Bagging1.4970.18800.964395.8170.66270.854294.667
      Model fusion5.0190.02870.994598.0160.27960.959096.333
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    Dongyu Xu, Xiaorun Li, Liaoying Zhao, Rui Shu, Qijia Tang. Hyperspectral Image Quality Evaluation Based on Multi-Model Fusion[J]. Laser & Optoelectronics Progress, 2019, 56(2): 021101

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    Paper Information

    Category: Imaging Systems

    Received: Jul. 1, 2018

    Accepted: Jul. 26, 2018

    Published Online: Aug. 1, 2019

    The Author Email: Li Xiaorun (lxr@zju.edu.cn)

    DOI:10.3788/LOP56.021101

    Topics

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