基于SIFT特征的SAR图像配准方法在玉树地震中的应用

本文针对SAR图像特点, 提出了基于改进SIFT(尺度不变特征变换)算法的SAR图像配准方案: ① 对待配准图像进行ISEF(无限对称指数滤波器)滤波处理, 降低图像的斑点噪声; ② 采用SIFT算法提取特征点, 略过差分金字塔第一层的特征点检测, 提高时间效率; ③ 在欧氏空间内剔除误匹配点, 提高配准精度。 实验表明, 本文提出的SAR图像配准方案检测到的匹配点对的数量和稳健性都有提高, 精度能够满足亚像元级SAR图像的应用需求, 且用时比传统SIFT方法减少60%以上。 最后对精配准的SAR图像进行震害变化检测, 得到的震害分布与高分辨率光学图像上判读的建筑物毁坏情况基本一致。

SAR Image Registration Based on SIFT Algorithm and its Application to the 2010 Yushu Earthquake

An improved matching method based on Scale Invariant Features Transform (SIFT) algorithm is proposed in this paper. The Infinite Symmetric Exponential Filter (ISEF) algorithm is adopted to reduce speckle noise before computation of the scale space pyramid. SIFT algorithm is utilized to detect the feature points and skip the first scale-space octave to reduce processing time. And then false matches are deleted in the Euclidean space. Experiments show that the proposed method increases the number of the features and improves the robustness. The match accuracy could meet the requirement of sub-pixel matching and the processing time has been cut by 60%. Finally, earthquake change detection is implemented from ALOS PALSAR images, and the building damage information detected is consistent with the results from high spatial resolution aerial image.