سابِق|تال

Remote sensing detection of water, snow and vegetation based on high information-background ratio and filter characteristics

  • role： First author第一作者
  • Affiliation：

    Guangxi Colleges and Universities Key Laboratory of Unmanned Aerial Vehicle (UAV) Remote Sensing, Guilin University of Aerospace Technology, Guilin 541004, China

  • Email：zhaohaimeng@163.com
  • Introduction：赵海盟，1980年生，男，副教授，研究方向为无人机遥感、偏振仪器、卫星导航、嵌入式系统设计。E-mail：zhaohaimeng@163.com
Haimeng ZHAO1,  
  • Affiliation：

    Beijing Key Laboratory of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, China

    Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China

  • Email：LIU Siyuan
Siyuan LIU23,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

  • Email：LI Junsheng
Junsheng LI4,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China

  • Email：WU Taixia
Taixia WU5,  
  • Affiliation：

    Finnish Geospatial Research Institute, 02431 Masala, Finland

  • Email：JouniPELTONIEMI
Peltoniemi Jouni6,  
  • Affiliation：

    Guangxi Colleges and Universities Key Laboratory of Unmanned Aerial Vehicle (UAV) Remote Sensing, Guilin University of Aerospace Technology, Guilin 541004, China

  • Email：HUANG Wentao
Wentao HUANG1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Beijing Key Laboratory of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, China

    Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China

  • Email：lyan@pku.edu.cn
  • Introduction：晏磊，1956年生，男，教授，研究方向为偏振与无人机仿生遥感、高分辨率成像与遥感定标、仿生导航。E-mail：lyan@pku.edu.cn
Lei YAN23*

ملخص

In optical remote sensing, the strong specular reflectivity and angle selectivity of water lead to detector saturation or to very low reflectivity for extracting effective information. The strong reflection characteristics and surface sensitivity of the snow limit the capability of the sensor to detect directly. Thus, water and snow are problems of passive remote sensing. The sensitivity of vegetation index under different reflection intensities in high-resolution quantitative remote sensing also challenges the accuracy and effectiveness of classical vegetation monitoring methods. This study aims to solve the bottleneck of water and snow that cannot be measured by optical remote sensing. This study uses the fourth law of remote sensing polarization effect, that is, high information–background ratio filtering characteristics. The polarization information can be obtained by adding the polarizer to the sensor in any direction. The Fessenkov method can be used to calculate the Degree Of Polarization (DOP) according to the data of different polarization angles and thus provide a new solution to the abovementioned remote sensing problems. The polarizing method can effectively enhance the information of the water-background ratio, which strips more than 70% of the glitter, provides the necessary method for the remote sensing of snow, and reduce up to 78% of error in vegetation monitoring. The mechanism of high-information-background ratio filtering is proved for the first time. Under the theoretical guidance and deepening of the experiment, the problems that water and snow can hardly be measured, and the bottleneck that vegetation cannot be accurately measured under strong reflection are solved.

مفهوم

polarization;information-background ratio;filter characteristics;water;snow;vegetation

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