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Remote Sens. 2017, 9(7), 686; https://doi.org/10.3390/rs9070686

An Advanced Rotation Invariant Descriptor for SAR Image Registration

1,2,* , 1
,
1,2
and
1,2
1
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100000, China
*
Author to whom correspondence should be addressed.
Received: 25 May 2017 / Revised: 18 June 2017 / Accepted: 2 July 2017 / Published: 4 July 2017
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Abstract

The Scale-Invariant Feature Transform (SIFT) algorithm and its many variants have been widely used in Synthetic Aperture Radar (SAR) image registration. The SIFT-like algorithms maintain rotation invariance by assigning a dominant orientation for each keypoint, while the calculation of dominant orientation is not robust due to the effect of speckle noise in SAR imagery. In this paper, we propose an advanced local descriptor for SAR image registration to achieve rotation invariance without assigning a dominant orientation. Based on the improved intensity orders, we first divide a circular neighborhood into several sub-regions. Second, rotation-invariant ratio orientation histograms of each sub-region are proposed by accumulating the ratio values of different directions in a rotation-invariant coordinate system. The proposed descriptor is composed of the concatenation of the histograms of each sub-region. In order to increase the distinctiveness of the proposed descriptor, multiple image neighborhoods are aggregated. Experimental results on several satellite SAR images have shown an improvement in the matching performance over other state-of-the-art algorithms. View Full-Text
Keywords: SAR; image registration; feature descriptor; rotation invariance SAR; image registration; feature descriptor; rotation invariance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xiang, Y.; Wang, F.; Wan, L.; You, H. An Advanced Rotation Invariant Descriptor for SAR Image Registration. Remote Sens. 2017, 9, 686.

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