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HIGH-PRECISION DEM PRODUCTION FOR SPACEBORNE STEREO SAR IMAGES BASED ON SIFT MATCHING AND REGION-BASED LEAST SQUARES MATCHING

机译:基于SIFT匹配和地区最小二乘匹配的基于SIFT匹配和区域的空间立体声SAR图像的高精度DEM生产

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Generally, there are two ways to generate Digital Elevation Model (DEM) using synthetic aperture radar (SAR) data, which are Interferometer Synthetic Aperture Radar(InSAR) and radargrammetry. Considering the disadvantages of In SAR data, such as the limit of terrain and the influence of water content, the application field of InSAR is relatively limited, while radargrammetry is more widely applied since it does not have such limits. However, for high-precision stereo SAR imagery, since the terrain distortion caused by shooting angle cannot be eliminated and the speckle noises are obvious, the classical matching algorithms for optical stereo images do not have the same effect on SAR data. Based on the experience of optical stereo image matching, this paper proposes a new algorithm which combines the feature of SIFT image matching, region-based least squares matching and TIN. First, SIFT matching is used as the initial matching to obtain the sparse DEM, then by using TIN the matching points are forecast, finally the region-based least squares matching is adopted to get accurate matching points. In this paper, COSMO-SkyMed and TSX stereo images of Lanzhou area are used to validate the proposed method. Experiment results show that the algorithm can be effectively used in stereo SAR matching and high-precision DEM production.
机译:通常,有两种方法可以使用合成孔径雷达(SAR)数据生成数字高度模型(DEM),这是干涉仪合成孔径雷达(INSAR)和RADARGRAMMMETRY。考虑到SAR数据的缺点,例如地形的极限和含水量的影响,insar的应用领域相对有限,而RadargramMetry更广泛地应用,因为它没有这样的限制。然而,对于高精度立体声SAR图像,由于不能消除由拍摄角度引起的地形失真并且散斑噪声显而易见,光学立体图像的经典匹配算法对SAR数据没有相同的影响。基于光学立体图像匹配的经验,本文提出了一种新的算法,它结合了SIFT图像匹配,基于区域的最小二乘匹配和锡的特征。首先,使用筛选匹配作为获得稀疏DEM的初始匹配,然后通过使用TIN匹配点,最后采用基于区域的最小二乘匹配来获得准确的匹配点。在本文中,兰州地区的COSMO-SCRED和TSX立体声图像用于验证所提出的方法。实验结果表明,该算法可以有效地用于立体声SAR匹配和高精度DEM生产。

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