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
Fig. 1. 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
Fig. 3. 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
Fig. 4. Spectra of the surface reflectance in the research region on 27 March
Fig. 5. The 7 examples of preprocessed surface reflectance spectra for different SSCs measured on 31 October 2019
Fig. 6. The relationships between the regularization hyperparameter λ,RMSE,MAPE and R2 for D’Sa
Fig. 7. The scatter diagrams
Fig. 8. The scatter diagrams
Fig. 9. SSC retrieval results of the baseline model
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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
Category: Research Articles
Received: Jan. 18, 2021
Accepted: --
Published Online: Apr. 18, 2022
The Author Email: Qing-Li LI (qlli@cs.ecnu.edu.cn)