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极轨气象卫星自动地标导航方法
The Automated Landmark Navigation of the Polar Meteorological Satellite
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摘要: 该文实现了一种极轨气象卫星的自动地标导航方法。地标导航能够纠正由于姿态而引起的定位误差。首先根据当前轨道遥感卫星图像中海洋、陆地、河流等地物特征能量的概率分布情况，利用全球模板，建立地标库，然后通过最大相关系数方法计算地标偏移量，从而获得姿态偏差，之后利用计算得到的姿态偏差对遥感卫星图像重新导航，获得地理定位结果。利用FY-1D扫描辐射计的遥感数据对方法进行检验，结果表明：该文所提出的自动地标导航方法可以有效纠正由姿态而引起的定位误差，达到像素级的定位精度。该方法能够突破传统地标导航方法需要丰富的遥感卫星历史资料的限制，拓展传统地标导航方法的适应范围。该方法已在我国2008年5月发射的新一代极轨气象卫星FY-3号上得到应用，并将在下一代静止轨道气象卫星FY-4号上进一步开发。Abstract: The problem of automated landmark navigation for the polar meteorological satellite is addressed. Automated landmark navigation can correct the systematic image navigation errors due to orbit, attitude and alignment matrix disturbance. Traditional automated landmark navigation methods need selecting the base image from long time series satellite imagery; otherwise it can result in registration displacements. Previous methods have shown that having daytime and nighttime (including early morning) base images for each season can minimize the registration error. The processing of one year data would thus require a minimum of eight base images for each location, and it limits the method from being widely applied. There are great needs for an accurate, easily implemented navigation system capable of automated landmark navigation when long time series satellite imagery data are absent.First, cloud detection methods insure that contamination from cloudy pixels is minimized. Second, the base image is composed of content features and structural features. The content features are constructed from the energy distribution of the current satellite image's ocean, land, rivers and so on. By defining the landmark feature point, the landmark's structural features are constructed from the global template. The landmark content and structural features are combined together to form the full base image. Third, the maximum cross correlation (MCC) method produces displacement vectors, which are translated into satellite attitude corrections to be added to the orbital image navigation corrections. Each resulting displacement vector has a correlation coefficient, which quantifies how well a pattern is matched. Displacements with correlations lower than the 95% confidence value is the elimination of error matching. The image navigation accuracies are also closely related to the landmark spatial distribution. The image navigation corrections are more accurate when the landmarks are average distributed through the whole satellite image. Finally, FY-1D satellite data are used to assess the performance. The testing results shows that this method is automatic and is successful to rectify the image navigation errors due to attitude disturbance and the rectified errors are within one pixel. This method does not use long time cache files needed by early methods and thus extend the applicability. The proposed method has been applied in Chinese next generation polar meteorological satellite FY-3 and will be developed further in the next generation geostationary meteorological satellite FY-4.
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图 3 地标生成过程
(其中海/陆槪率分布采用的数据是2005年6月20日23:37 (世界时) FY-1D扫描辐射计第4通道数据)
Fig. 3 Examples of landmark whose energy distribution data is from the FY-1D scanning radiomoter 4th band data at 23:37 20 June 2005
图 4 俄罗斯奥廖克明斯克附近的勒拿河FY-1D扫描辐射计遥感图像定位结果
Fig. 4 FY-1D scanning radiometer image navigation of Lena near Olokminsk in Russia
图 5 俄罗斯阿尔丹河FY-1D扫描辐射计遥感图像定位
Fig. 5 FY-1D scanning radiometer image navigation of Aldan River
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