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Using Dual-Polarization Interferograms to Correct Atmospheric Effects for InSAR Topographic Mapping

机译:使用双极化干涉图校正InSAR地形图的大气影响

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Atmospheric effect represents one of the major error sources for interferometric synthetic aperture radar (InSAR), particularly for the repeat-pass InSAR data. In order to further improve the practicability of InSAR technology, it is essential to study how to estimate and eliminate the undesired impact of atmospheric effects. In this paper, we propose the multi-resolution weighted correlation analysis (MRWCA) method between the dual-polarization InSAR data to estimate and correct atmospheric effects for InSAR topographic mapping. The study is based on the a priori knowledge that atmospheric effects is independent of the polarization. To find the identical atmospheric phase (ATP) signals of interferograms in different polarizations, we need to remove the other same or similar phase components. Using two different topographic data, differential interferometry was firstly performed so that the obtained differential interferograms (D-Infs) have different topographic error phases. A polynomial fitting method is then used to remove the orbit error phases. Thus, the ATP signals are the only identical components in the final obtained D-Infs. By using a forward wavelet transform, we break down the obtained D-Infs into building blocks based on their frequency properties. We then applied weighted correlation analysis to estimate the wavelet coefficients attributed to the atmospheric effects. Thus, the ATP signals can be obtained by the refined wavelet coefficients during inverse wavelet transform (IWT). Lastly, we tested the proposed method by the L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR dual-polarization SAR data pairs covering the San Francisco (USA) and Moron (Mongolia) regions. By using Ice, Cloud, and land Elevation Satellite (ICESat) data as the reference data, we evaluated the vertical accuracy of the InSAR digital elevation models (DEMs) with and without atmospheric effects correction, which shows that, for the San Francisco test site, the corrected interferogram could provide a DEM with a root-mean-square error (RMSE) of 7.79 m, which is an improvement of 40.5% with respect to the DEM without atmospheric effects correction. For the Moron test site, the corrected interferogram could provide a DEM with an RMSE of 10.74 m, which is an improvement of 30.2% with respect to the DEM without atmospheric effects correction.
机译:大气效应是干涉合成孔径雷达(InSAR)的主要误差源之一,特别是对于重复通过InSAR数据而言。为了进一步提高InSAR技术的实用性,研究如何估计和消除大气效应的不良影响至关重要。在本文中,我们提出了双极化InSAR数据之间的多分辨率加权相关分析(MRWCA)方法,以估计和校正InSAR地形图的大气影响。该研究基于先验知识,即大气效应与极化无关。为了找到不同偏振态的干涉图的相同大气相位(ATP)信号,我们需要去除其他相同或相似的相位分量。首先使用两个不同的地形数据,进行差分干涉测量,以便获得的差分干涉图(D-Infs)具有不同的地形误差相位。然后使用多项式拟合方法去除轨道误差相位。因此,ATP信号是最终获得的D-Infs中唯一相同的成分。通过使用前向小波变换,我们基于获得的D-Infs的频率特性将其分解为构建块。然后,我们应用加权相关分析来估计归因于大气效应的小波系数。因此,可以在逆小波变换(IWT)期间通过细化的小波系数来获得ATP信号。最后,我们通过L波段高级陆地观测卫星(ALOS)-1 PALSAR双极化SAR数据对测试了该方法,该数据对覆盖了旧金山(美国)和莫龙(蒙古)地区。通过使用冰,云和陆地高程卫星(ICESat)数据作为参考数据,我们评估了带有和不带有大气影响校正的InSAR数字高程模型(DEM)的垂直精度,这表明,对于旧金山测试站点,校正后的干涉图可以为DEM提供7.79 m的均方根误差(RMSE),相对于未经大气效应校正的DEM而言,该误差提高了40.5%。对于Moron测试点,校正后的干涉图可以提供RMSE为10.74 m的DEM,相对于没有校正大气影响的DEM而言,其提高了30.2%。

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