Despite impressive progress in the past decade, accurate and efficient multiview synthetic aperture radar (SAR) image registration remains a challenging task due to complex imaging mechanisms and various imaging conditions. Especially, for rugged areas, SAR images obtained from the opposite-side view reflect different characteristics, making popular SAR image registration methods no longer applicable. To this end, we propose a geometry-aware image registration method by extracting inherent orientation features and concentrating on geometry-invariant areas. First, slant range images are terrain-corrected using a digital elevation model (DEM) to reduce large relative positioning errors caused by elevation. Second, the Gabor-ratio detector is introduced to obtain multiscale orientation features, which are more robust under various imaging conditions. Then, a geometry-aware mask is produced by intersecting the 3-D space ray with DEM, and thus, SAR images can be divided into three categories, layover, shadow, and geometry-invariant areas. The geometry-aware matching method, which focuses on geometry-invariant areas and masks out misleading caused by geometric and radiometric distortions, is proposed to realize accurate matching. The rational polynomial coefficients (RPCs) are refined to achieve relative correction. Extensive results on dozens of SAR images demonstrate the effectiveness and universality of the proposed algorithm by quantitative evaluation using man-made and natural corner reflectors. An analysis of the factors affecting registration accuracy is also discussed.

中文翻译：

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多视点高分辨率SAR图像的几何感知配准算法


尽管在过去十年中取得了令人瞩目的进展，但由于复杂的成像机制和各种成像条件，准确高效的多视图合成孔径雷达（SAR）图像配准仍然是一项具有挑战性的任务。特别是对于崎岖不平的地区，从对侧视角获得的SAR图像反映了不同的特征，使得流行的SAR图像配准方法不再适用。为此，我们通过提取固有方向特征并专注于几何不变区域，提出了一种几何感知图像配准方法。首先，使用数字高程模型（DEM）对斜距图像进行地形校正，以减少因高程引起的较大相对定位误差。其次，引入Gabor比探测器来获得多尺度方向特征，这些特征在各种成像条件下都更加鲁棒。然后，通过将 3-D 空间射线与 DEM 相交来生成几何感知掩模，因此 SAR 图像可以分为三类：重叠区域、阴影区域和几何不变区域。提出了几何感知匹配方法，该方法关注几何不变区域并掩盖几何和辐射失真引起的误导，以实现精确匹配。有理多项式系数（RPC）经过细化以实现相对校正。通过使用人造和自然角反射器进行定量评估，对数十张 SAR 图像的广泛结果证明了该算法的有效性和普适性。还讨论了影响配准精度的因素的分析。