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高信息—背景反差比滤波特性的水、雪、植被偏振遥感探测
引用本文:赵海盟,刘思远,李俊生,吴太夏,Jouni Peltoniemi,黄文韬,晏磊.高信息—背景反差比滤波特性的水、雪、植被偏振遥感探测[J].遥感学报,2018,22(6):957-968.
作者姓名:赵海盟  刘思远  李俊生  吴太夏  Jouni Peltoniemi  黄文韬  晏磊
作者单位:桂林航天工业学院 广西高校无人机遥测重点实验室, 桂林 541004,北京大学 空间信息集成与3S工程应用北京市重点实验室, 北京 100871;北京大学 地球与空间科学学院 遥感与地理信息系统研究所, 北京 100871,中国科学院 遥感与数字地球研究所, 北京 100094,河海大学 地球科学与工程学院, 南京 210098,Finnish Geospatial Research Institute, 02431 Masala, Finland,桂林航天工业学院 广西高校无人机遥测重点实验室, 桂林 541004,北京大学 空间信息集成与3S工程应用北京市重点实验室, 北京 100871;北京大学 地球与空间科学学院 遥感与地理信息系统研究所, 北京 100871
基金项目:国家自然科学基金(编号:41371492);国家重点研发计划项目(编号:2017YFB0503003);广西自然科学基金重点项目(编号:2016JJD110017)
摘    要:在光学遥感中,水的强烈镜面反射性和角度选择性使探测器饱和或反射率过低而难以提取有效信息,雪的强反射性和表面敏感性使传感器难以直接探测,植被指数在不同反射强度下的敏感性对经典植被监测方法的精度和有效性提出挑战。偏振手段可大大提高水、雪和植被的遥感识别能力。本文利用地物遥感偏振光效应的高信息—背景反差比滤波特性,解决光学遥感中水、雪的不可测量问题,以及破除植被强光反射条件下无法精细监测的瓶颈。本文从偏振高信息—背景反差比滤波特性理论出发,通过实验证明偏振手段可有效提升水的信息—背景反差比、剥离70%以上的太阳耀光,为强反射特性下的积雪遥感提供必要方法,并最高降低78%的植被监测误差。本文首次推导证明了偏振探测高信息—背景反差比滤波特性机理,在理论指导和实验深化引导下解决了光学遥感中水、雪因探测器饱和而无法测量的问题,并破除了强反射条件下植被无法精细监测的瓶颈。

关 键 词:偏振  信息—背景反差比  滤波特性      植被
收稿时间:2017/9/3 0:00:00

Remote sensing detection of water, snow and vegetation based on high information-background ratio and filter characteristics
ZHAO Haimeng,LIU Siyuan,LI Junsheng,WU Taixi,Jouni Peltoniemi,HUANG Wentao and YAN Lei.Remote sensing detection of water, snow and vegetation based on high information-background ratio and filter characteristics[J].Journal of Remote Sensing,2018,22(6):957-968.
Authors:ZHAO Haimeng  LIU Siyuan  LI Junsheng  WU Taixi  Jouni Peltoniemi  HUANG Wentao and YAN Lei
Institution:Guangxi Colleges and Universities Key Laboratory of Unmanned Aerial Vehicle (UAV) Remote Sensing, Guilin University of Aerospace Technology, Guilin 541004, China,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,Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China,School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China,Finnish Geospatial Research Institute, 02431 Masala, Finland,Guangxi Colleges and Universities Key Laboratory of Unmanned Aerial Vehicle (UAV) Remote Sensing, Guilin University of Aerospace Technology, Guilin 541004, China and 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
Abstract: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.
Keywords:polarization  information-background ratio  filter characteristics  water  snow  vegetation
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