Images registration for an optical and synthetic aperture radar (SAR) is a key of multisensor images analysis. Also, parameters selection affects the final result of the registration algorithms for optical and SAR images. For images obtained by different sensors, how to choose an appropriate parameter for accurate registration is a key problem. In this letter, a parameter adaptive registration algorithm between optical and SAR images based on scale-invariant feature transform (SIFT) is proposed. Because of the different imaging mechanisms of these two kinds of images, the algorithm uses the multiscale Sobel operator to calculate the gradient for the optical image, while for the SAR image, a new adaptive operator based on the neighborhood pixel value is proposed to calculate the gradient. In the feature extraction, the adaptive value estimated by constant false alarm rate (CFAR) detection is used instead of the fixed threshold. Finally, a matching method constrained by image size scaling [scale-constrained fast sample consensus (FSC)] is proposed. The evaluation was designed in two aspects: feature extraction and image registration. The algorithm we proposed shows excellent performances in the aspects of repeatability, correct matching rate (CMR), and root mean square error (RMSE). The experimental results show that our method maintains the performance of the original algorithm and has some optimization and breakthrough.

中文翻译：

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使用尺度约束匹配方法自适应配准光学和 SAR 图像


光学和合成孔径雷达（SAR）的图像配准是多传感器图像分析的关键。此外，参数的选择也会影响光学和SAR图像配准算法的最终结果。对于不同传感器获得的图像，如何选择合适的参数进行精确配准是一个关键问题。在这封信中，提出了一种基于尺度不变特征变换（SIFT）的光学图像与SAR图像之间的参数自适应配准算法。由于这两类图像的成像机制不同，算法对于光学图像采用多尺度Sobel算子计算梯度，而对于SAR图像，提出了一种新的基于邻域像素值的自适应算子来计算梯度坡度。在特征提取中，使用恒定虚警率（CFAR）检测估计的自适应值来代替固定阈值。最后，提出了一种受图像尺寸缩放约束的匹配方法[尺度约束快速样本一致性（FSC）]。评估设计分为特征提取和图像配准两个方面。我们提出的算法在重复性、正确匹配率（CMR）和均方根误差（RMSE）方面表现出优异的性能。实验结果表明，我们的方法保持了原有算法的性能，并且有一定的优化和突破。