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Volume 43 Issue 6
Jun.  2021
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WANG Guangyuan, DENG Zhengdong, LU Zhao, WANG Daqing, SHI Yue, XU Haoli, ZHAO Xiaoning. Snow Information Recognition based on GF-6 PMS Images[J]. Infrared Technology , 2021, 43(6): 543-556.
Citation: WANG Guangyuan, DENG Zhengdong, LU Zhao, WANG Daqing, SHI Yue, XU Haoli, ZHAO Xiaoning. Snow Information Recognition based on GF-6 PMS Images[J]. Infrared Technology , 2021, 43(6): 543-556.
shu

Snow Information Recognition based on GF-6 PMS Images

  • College of National Defense Engineering, The Army Engineering University of PLA, Nanjing 210007, China

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  • Received Date: November 04, 2020
  • Revised Date: December 19, 2020
    Graphical Abstract
    • Abstract
  • From the perspective of limited research on snow recognition in high spatial resolution optical remote sensing images, considering Daowai District of Harbin City as the research area, this paper systematically discusses and analyzes the application of different methods in snow information recognition. First, ground feature types and snow distribution characteristics were mastered through visual interpretation of GF-6 PMS images of two phases in the study area. Second, based on the results of visual interpretation, eight typical surface feature types were selected, and the spectral characteristics of "snow"and "non-snow"pixels were obtained. Third, the application of the six methods in snow recognition was discussed and analyzed. Accuracy was evaluated using three indexes: positive predictive value, recall rate, and F-score. Finally, a final recognition result judgment method based on voting results was developed, and the final recognition result of snow information in the study area was obtained. The results showed that, owing to the influence of the underlying surface and shadow, the phenomenon of "same spectrum foreign matter"and "same object different spectrum"was common in the study area, which interfered with the snow recognition process to a large extent. The recognition effect of the deep learning algorithm was the best, while that of the decision tree method was relatively poor; the recognition accuracy was higher for the farmland area than that for the pond area, and the phenomenon of false and missing recognitions was relatively less. The final recognition result judgment method based on voting results can effectively improve the phenomenon of false and missing recognitions in a single recognition method. This paper has important guiding significance for snow recognition based on high-spatial-resolution optical remote sensing images.
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