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Remote Sens. 2017, 9(9), 882; doi:10.3390/rs9090882

Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
3
School of Geomatics, East China University of Technology, Nanchang 330013, China
4
The Third Geodetic Brigade of National Administration of Surveying, Mapping and Geoinformation, Chengdu 610100, China
*
Author to whom correspondence should be addressed.
Received: 14 July 2017 / Revised: 22 August 2017 / Accepted: 23 August 2017 / Published: 25 August 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Improving the matching reliability of multi-sensor imagery is one of the most challenging issues in recent years, particularly for synthetic aperture radar (SAR) and optical images. It is difficult to deal with the noise influence, geometric distortions, and nonlinear radiometric difference between SAR and optical images. In this paper, a method for SAR and optical images matching is proposed. First, interest points that are robust to speckle noise in SAR images are detected by improving the original phase-congruency-based detector. Second, feature descriptors are constructed for all interest points by combining a new Gaussian-Gamma-shaped bi-windows-based gradient operator and the histogram of oriented gradient pattern. Third, descriptor similarity and geometrical relationship are combined to constrain the matching processing. Finally, an approach based on global and local constraints is proposed to eliminate outliers. In the experiments, SAR images including COSMO-Skymed, RADARSAT-2, TerraSAR-X and HJ-1C images, and optical images including ZY-3 and Google Earth images are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method provides significant improvements in the number of correct matches and matching precision compared with the state-of-the-art SIFT-like methods. Near 1 pixel registration accuracy is obtained based on the matching results of the proposed method. View Full-Text
Keywords: image matching; SAR image; optical image; phase congruency; Gaussian-Gamma-shaped bi-windows; geometric constraint image matching; SAR image; optical image; phase congruency; Gaussian-Gamma-shaped bi-windows; geometric constraint
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Chen, M.; Habib, A.; He, H.; Zhu, Q.; Zhang, W. Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint. Remote Sens. 2017, 9, 882.

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