应用于SAR图像配准的自适应SIFT特征均匀分布算法
Auto-Adaptive Well-Distributed Scale-Invariant Feature for SAR Images Registration
-
摘要: SAR图像配准要求提取出稳定且分布均匀的同名点,但是传统的SIFT算法没有考虑特征点的空间分布情况,提取的特征呈块状分布。针对SAR图像数据的特点,提出了一种自适应控制SIFT特征均匀分布算法,利用局部纹理特征,并结合最优化筛选策略,在保证特征点稳定性和准确性的同时,自适应控制特征在不同空间的分布情况,实现SIFT特征点在图像空间和尺度空间的合理分布。最后从提取效率、匹配正确率和分布质量等方面进行对比试验,验证了算法应用于SAR图像配准的能力。Abstract: Registration of SAR images needs well-distributed and reliable matching point pairs，but the traditional Scale-Invariant Feature Transform(SIFT) approach has limitations related to the distribution of extracted features. A well-distributed auto-adaptive SIFT matching algorithm for SAR images is proposed，based on local textures，using a double-threshold-optimization selection strategy.The proposed algorithm auto-adaptively controls the distribution of the extracted feature points and maintained reliable and precise in both the image and scale space. Comprehensive evaluation of the efficiency，distribution quality，and matching accuracy of the extracted point pairs on a variety of SAR images demonstrates the capabilities of the proposed algorithm.
下载:
