SAR Image Registration Using Phase Congruency and Nonlinear Diffusion-Based SIFT

Fan, Jianwei; Wu, Yan; Wang, Fan; Zhang, Qiang; Liao, Guisheng; Li, Ming

doi:10.1109/lgrs.2014.2351396

Cited by 81 publications

(14 citation statements)

References 12 publications

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“…The GLOH descriptor has been successfully applied to many registration problems and shown to be efficient [7], [15], [27]. For SAR image registration, given speckle noise, it is unreliable to consider a single-scale patch to characterize feature points.…”

Section: A Novel Multiscale Circle Descriptor (Mcd)mentioning

confidence: 99%

“…We found that different values of the response threshold SH d of the Harris function mentioned in Section III-C greatly impact the experimental results. Thus, we test the four algorithms based on the Harris corner detector; namely, SAR-SIFT [7], NDSS-SIFT [27], our method without MCD, and our method with MCD. Fig.…”

Section: Performance Of the Harris Function For Different Response Thresholdsmentioning

confidence: 99%

“…Wang et al [26] combined the bilateral filter (BF) with building scale space and then proposed a dual-matching strategy to ameliorate the results of experiments. Fan et al [27] utilized nonlinear diffusion to generate the scale space and phase congruency to remove outliers to improve SAR matching performance.…”

Section: Introductionmentioning

confidence: 99%

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SAR Image Registration Based on ROEWA-Blocks and Multiscale Circle Descriptor

Hong

Leng

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Given the imaging characteristics of synthetic aperture radar (SAR) images and the inherent speckle noise in them, scaleinvariant feature transform (SIFT) based algorithms are unable to perform satisfactorily. To improve registration efficiency between SAR images, we propose a robust and efficient registration method with three main contributions. First, considering sudden dark patches appearing in SAR images, we propose the ratio of exponentially weighted average blocks to suppress the sudden dark patches and better adapt to different test images. This new operator called blocks of the ratio of exponentially weighted averages (ROEWA-B), divides the processing windows of ROEWA into blocks, which can not only reduce speckle noise but also retain more edge details compared to ROEWA when sudden dark patches appear. Second, for outlier removal, we present an approach using the minimum moment map to remove erroneous keypoints. Finally, based on the gradient location orientation histogram descriptor, we propose a novel multiscale circle descriptor, which combines scale change information to give weights to feature points at different scales. Experimental results for various thresholds and evaluations demonstrate the advantage and robustness of our method in registration.Index Terms-SAR image registration, ratio of exponentially weighted averages (ROEWA), multiscale space, multiscale descriptor.

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Section: A Novel Multiscale Circle Descriptor (Mcd)mentioning

confidence: 99%

Section: Performance Of the Harris Function For Different Response Thresholdsmentioning

confidence: 99%

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SAR Image Registration Based on ROEWA-Blocks and Multiscale Circle Descriptor

Hong

Leng

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, the data required smoothing in prior to accommodate atmospheric noise. (Fan et al, 2014) used the model in registering SAR (Synthetic Aperture Radar) images. They used nonlinear diffusion scale space along with the ratio of exponential weighted average operator to reduce noise and preserve fine details in the image.…”

Section: Overviewmentioning

confidence: 99%

Semi-Automatic Approach for Optical and Lidar Data Integration Using Phase Congruency Model at Multiple Resolutions

Megahed

Yan

Shaker

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In light of the ongoing urban sprawl reported in recent studies, accurate urban mapping is essential for assessing current status and evolve new policies, to overcome various social, environmental, and economic consequence. Imagery and LiDAR data integration densifies remotely sensed data with radiometric and geometric characteristics, respectively, for a precise segregation of different urban features. This study integrated aerial and LiDAR images using point primitives, which were obtained from running the Phase Congruency model as an image filter to detect edges and corner. The main objective is to study the effect of applying the filter at different spatial resolutions on the registration accuracy and processing time. The detected edge/corner points that are mutual in both datasets, were identified as candidate points. The Shape Context Descriptor method paired-up candidate points as final points based on a minimum correlation of 95%. Affine, second and third order polynomials, in addition to the Direct Linear Transformation models were applied for the image registration process using the two sets of final points. The models were solved using Least Squares adjustments, and validated by a set of 55 checkpoints. It was observed that with the decrease in spatial resolution, on one hand, the registration accuracy did not significantly vary. However, the consistency of the model development and model validation accuracies were enhanced, especially with the third order polynomial model. On the other hand, the number of candidate points decreased; consequently, the processing time significantly declined. The 3D LiDAR points were visualised based on the Red, Green, and Blue radiometric values that were inherited from the aerial photo. The qualitative inspection was very satisfactory, especially when examining the scene’s tiny details. In spite of the interactivity in determining the candidate points, the proposed procedure overcomes the dissimilarity between datasets in terms of acquisition technique and time, and widens the tolerance of accepting points as candidates by including points that are not traditionally considered (i.e. road intersections).

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“…However, it fails to provide favorable results while dealing with the SAR images. The reason is the fact that Gaussian blurring is one instance of isotropic diffusion filtering which is sensitive to speckle noise and does not respect the natural boundaries of the object 7 . As a consequence, many unstable keypoints are brought from the Gaussian scale space of SIFT and then the matching performance is degraded.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

2016

J. Appl. Remote Sens

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.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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SAR Image Registration Using Phase Congruency and Nonlinear Diffusion-Based SIFT

Cited by 81 publications

References 12 publications

SAR Image Registration Based on ROEWA-Blocks and Multiscale Circle Descriptor

SAR Image Registration Based on ROEWA-Blocks and Multiscale Circle Descriptor

Semi-Automatic Approach for Optical and Lidar Data Integration Using Phase Congruency Model at Multiple Resolutions

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

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