基于相干斑抑制SIFT的SAR图像配准方法

基于尺度不变特征变换(scale invariant feature transform, SIFT)算法,提出了一种能有效抑制相干斑噪声干扰的合成孔径雷达图像配准方法。该方法首先基于相干斑抑制各向异性扩散滤波模型建立图像的各向异性尺度空间,在滤除斑点噪声的同时保持了图像细节,弱化了斑点噪声对特征提取的影响;然后采用改进的二元直方图分析方法优化双向匹配初始结果,剔除了随机分布的误匹配点;最后引入临近特征点变换误差分析的过程,增加正确匹配点对数量,提高了变换模型参数的准确度。实验结果表明,该方法能增强SIFT特征点的稳定性,取得较高的配准精度,对相干斑噪声具有良好的适应性。

SAR image registration algorithm based on speckle reducing SIFT

Based on the scale invariant feature transform (SIFT) algorithm, a synthetic aperture radar (SAR) image registration algorithm which can effectively suppress the interference of speckle noise is proposed. Firstly, the anisotropic scale space of the image is constructed by the speckle reducing anisotropic diffusion, so that spackle noise is greatly reduced while prominent structures of the images are preserved, which weakens the impact of speckle noise on feature extraction. Secondly, the bivariate histogram analysis method is improved to refine the initial feature matches obtained by the dual matching strategy, which eliminates randomly distributed false correspondences. Finally, a reliable method of transformation error analysis between adjacent features is introduced to increase the number of correct matches, which improves the accuracy of the transformation parameters. Experiments demonstrate the applicability of the proposed algorithm to enhance the stability of SIFT features and achieve high registration accuracy. The results show that the proposed algorithm has a high adaptability to speckle noise.