Journal of Infrared and Millimeter Waves, Volume. 41, Issue 1, 2021015(2022)

A neural networks based method for suspended sediment concentration retrieval from GF-5 hyperspectral images

Yi-Ming LIU1, Lei ZHANG2, Mei ZHOU1,3,4, Jian LIANG5, Yan WANG1, Li SUN1,3, and Qing-Li LI1,3,4、*
Author Affiliations
  • 1Shanghai Key Laboratory of Multidimensional Information Processing,East China Normal University,Shanghai 200241,China
  • 2Beijing Tracking and communication Technology Institute,Beijing 100094,China
  • 3Engineering Center of SHMEC for Space Information and GNSS,Shanghai 200241,China
  • 4Engineering Research Center of Nanophotonics & Advanced Instrument,Ministry of Education,East China Normal University,Shanghai 200241,China
  • 5Nantong Academy of Intelligent Sensing,Nantong 226000,China
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    Figures & Tables(12)
    Locations of 14 SSC field measurements on March 27(blue),May 24(brown)and 31 October(black)2019 near the Yangtze estuarine and coastal waters. The stars and diamonds represent the field measurements collected by the buoy stations and ships,respectively
    Flow diagram for the entire SSC retrieval process.
    Line chart of total in situ SSC data. The number 1~7,8~10,11~14 samples were measured on 31 October,24 May and 27 March 2019,separately. A separation line(purple)is plotted to highlight the water samples 1~7 used for the final retrieval. The blue to yellow colors of dots intuitively show the low to high SSC levels. The lines drew in blue and orange represent the origin SSC values of all 3 days and sorted SSC values of 31 October 2019,respectively
    Spectra of the surface reflectance in the research region on 27 March (a) 24 May (b) and 31 October (c) 2019. The dotted,dashed and solid lines represent the low,middle and high SSC values,respectively (d) some surface reflectance spectra extracted from different typical ground objects on 31 October 2019
    The 7 examples of preprocessed surface reflectance spectra for different SSCs measured on 31 October 2019
    The relationships between the regularization hyperparameter λ,RMSE,MAPE and R2 for D’Sa (a) Nechad (b) Ruhl (c) and Loisel (d) models in the application for baseline model calibration
    The scatter diagrams (left) between the predicted values and field measurement values and the NNC calibration curves (right) for D’Sa (a),Nechad (b),Ruhl(c) and Loisel(d) models in the application for baseline model calibration
    The scatter diagrams (left) between the predicted values and field measurement values and the NNC calibration curves (right) for D’Sa (a),Nechad (b),Ruhl (c) and Loisel (d) models in the application for temporal calibration
    SSC retrieval results of the baseline model (a) and NNC double calibration (b) using the D’Sa model in the application of temporal calibration based on the GF-5 images in the Yangtze estuarine and coastal waters on 31 October 2019. For result comparison,the magnified images of the region of interest(ROI)labelled in the red area are provided in the top left of each picture. The green star and pink diamond denote the samples with 0.14 and 0.63 g/L SSC values,respectively
    • Table 1. Main parameters for GF-5 AHSI

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      Table 1. Main parameters for GF-5 AHSI

      ParametersCapability
      Spatial Coverage60 km
      Spectral Range400~ 2500 nm
      Spectral ResolutionVNIR:5 nm,SWIR:10 nm
      Spatial Resolution30 m
      Signal to Noise100~200
    • Table 2. Comparison between baseline and NNC results in the application for baseline model calibration.

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      Table 2. Comparison between baseline and NNC results in the application for baseline model calibration.

      Modeling MethodIndependent Variables(nm)BaselineNNC
      RMSE(g/L)MAPER2RMSE(g/L)MAPER2
      D’Sa668,5490.14950.78210.68050.14360.75800.6926
      Nechad7580.15870.80490.67290.15670.76570.6772
      Ruhl7450.21041.11420.60390.19390.98490.6336
      Loisel557,489,6680.49412.58120.29140.39932.19950.3992
    • Table 3. Comparison between baseline and NNC results in the application for temporal calibration

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      Table 3. Comparison between baseline and NNC results in the application for temporal calibration

      Modeling MethodIndependent Variables(nm)BaselineNNC
      RMSE(g/L)MAPER2RMSE(g/L)MAPER2
      D’Sa668,5490.12180.86570.66880.13520.78170.7155
      Nechad7620.31660.70160.40830.15880.76830.6670
      Ruhl7620.29930.58670.39780.18040.99470.6456
      Loisel557,489,6680.41600.69720.36850.36153.5580.3037
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    Yi-Ming LIU, Lei ZHANG, Mei ZHOU, Jian LIANG, Yan WANG, Li SUN, Qing-Li LI. A neural networks based method for suspended sediment concentration retrieval from GF-5 hyperspectral images[J]. Journal of Infrared and Millimeter Waves, 2022, 41(1): 2021015

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    Paper Information

    Category: Research Articles

    Received: Jan. 18, 2021

    Accepted: --

    Published Online: Apr. 18, 2022

    The Author Email: Qing-Li LI (qlli@cs.ecnu.edu.cn)

    DOI:10.11972/j.issn.1001-9014.2022.01.029

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