SAR Image Registration Based on Multifeature Detection and Arborescence Network Matching

• Published in: IEEE geoscience and remote sensing letters Vol. 13; no. 5; pp. 706 - 710
• Main Authors: Zhu, Hao, Ma, Wenping, Hou, Biao, Jiao, Licheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-05-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arborescence network; Feature extraction; Image registration; Radar imaging; Robustness; scale-invariant feature transform (SIFT); Speckle; Synthetic aperture radar; synthetic aperture radar (SAR); Transforms; Arborescence network; scale-invariant feature transform (SIFT); synthetic aperture radar (SAR); image registration
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2016.2539207

In this letter, a novel synthetic aperture radar (SAR) image registration method, including two operators for feature detection and arborescence network matching (ANM) for feature matching, is proposed. The two operators, namely, SAR scale-invariant feature transform (SIFT) and R-SIFT, can detect corner points and texture points in SAR images, respectively. This process has an advantage of preserving two types of feature information in SAR images simultaneously. The ANM algorithm has a two-stage process for finding matching pairs. The backbone network and the branch network are successively built. This ANM algorithm combines feature constraints with spatial relations among feature points and possesses a larger number of matching pairs and higher subpixel matching precision than the original version. Experimental results on various SAR images show that the proposed method provides superior performance than other approaches investigated.