A New Local Feature Descriptor for SAR Image Matching
Because of the weather-and illumination-independent characteristics, Synthetic Aperture Radar (SAR) has been playing a more important role for target recognition. Local stable feature descriptors in SAR image matching have been a interesting field in recent years. A new local feature extraction method like Scale Invariant Feature Transformation (SIFT) is proposed in this presentation, in which Local Gradient Ratio Pattern Histogram (LGRPH) based on SAR image similarity are taken as local feature descriptor from the neighbourhood of key points. Firstly, we extract the keypoints in difference of guassian (DoG) scale pyramid like many modified SAR-SIFT methods. Secondly, in the neighbour of kepoints, the local gradient ratio pattern histogram (LGRPH) is computed individually. Finally, the similarity is obtained by utilizing K-L discrepancy to measure the distance of LGRPH. Experimental results based on synthetic and real SAR images demonstrate that the proposed approach is robust to the speckle noise and local gradient variation in SAR images.
Tao Tang Deliang Xiang Yi Su
College of Electronic Science & Engineering, National University of Defense Technology, Changsha, China
国际会议
Progress in Electromagnetics Research Symposium 2014(2014年电磁学研究新进展学术研讨会)
广州
英文
1823-1827
2014-08-01（万方平台首次上网日期，不代表论文的发表时间）