基于非线性尺度空间SIFT的SAR图像特征点提取

针对SAR图像特征提取中的相干斑问题，提出一类非线性尺度空间的构造方法，用于提取具有尺度不变特性的特征点。首先，利用SAR图像特有的频域多视处理，代替尺度空间中的空域降采样，构造多分辨率图像，提高了辐射分辨率，抑制了相干斑。其次，基于改进的增强Lee滤波，建立非线性尺度空间，在保留特征细节的同时，进一步抑制了相干斑。 实验结果表明，该方法相比典型的高斯尺度空间和一般的非线性尺度空间，能更好的抑制相干斑噪声，提高了特征点提取数量和正确匹配点数量。

SAR image feature extraction based on nonlinear scale space SIFT

To address the problem caused by speckle noise in SAR image feature extraction, a method based on nonlinear scale space is proposed, to extract scale invariant feature points. First, instead of down sample in scale space construction, multi look process is utilized to build multi solution images, which improves radiometric resolution and reduces speckle noise. Then, a nonlinear scale space is constructed based on a novel enhanced Lee filter, which preserves feature details as well as reducing speckle noise further. The experiment shows that the proposed method is not only perform better in speckle noise suppression, but also increase the amount of feature points and amount of correctly matched points.