Remote Sensing Applications and Decision Support

Speckle-reducing scale-invariant feature transform match for synthetic aperture radar image registration

[+] Author Affiliations
Xianmin Wang, Bo Li, Qizhi Xu

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, Xueyuan Road No. 37, Haidian District, Beijing 100191, China

Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Xueyuan Road No. 37, Haidian District, Beijing 100191, China

J. Appl. Remote Sens. 10(3), 036030 (Sep 26, 2016). doi:10.1117/1.JRS.10.036030
History: Received May 26, 2016; Accepted September 2, 2016
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Abstract.  The anisotropic scale space (ASS) is often used to enhance the performance of a scale-invariant feature transform (SIFT) algorithm in the registration of synthetic aperture radar (SAR) images. The existing ASS-based methods usually suffer from unstable keypoints and false matches, since the anisotropic diffusion filtering has limitations in reducing the speckle noise from SAR images while building the ASS image representation. We proposed a speckle reducing SIFT match method to obtain stable keypoints and acquire precise matches for the SAR image registration. First, the keypoints are detected in a speckle reducing anisotropic scale space constructed by the speckle reducing anisotropic diffusion, so that speckle noise is greatly reduced and prominent structures of the images are preserved, consequently the stable keypoints can be derived. Next, the probabilistic relaxation labeling approach is employed to establish the matches of the keypoints then the correct match rate of the keypoints is significantly increased. Experiments conducted on simulated speckled images and real SAR images demonstrate the effectiveness of the proposed method.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Xianmin Wang ; Bo Li and Qizhi Xu
"Speckle-reducing scale-invariant feature transform match for synthetic aperture radar image registration", J. Appl. Remote Sens. 10(3), 036030 (Sep 26, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036030


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