SAR Image Registration Using Multiscale Image Patch Features With Sparse Representation

Fan, Jianwei; Wu, Yan; Li, Ming; Liang, Wenkai; Zhang, Qiang

doi:10.1109/jstars.2016.2628911

Cited by 33 publications

(11 citation statements)

References 25 publications

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“…SAR imaging has numerous applications that are divided into two categories: amplitude‐based and phase‐based. Radargrammetry [1], change detection [2], image registration [3, 4] and image fusion are amplitude‐based applications, while interferometry [5, 6] is the most important phase‐based application. SAR image matching is a major processing step in all of the aforementioned applications.…”

Section: Introductionmentioning

confidence: 99%

Optimal texture image reconstruction method for improvement of SAR image matching

Ghannadi¹,

Saadatseresht²,

Izadi

et al. 2020

IET Radar, Sonar & Navigation

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Section: Introductionmentioning

confidence: 99%

Optimal texture image reconstruction method for improvement of SAR image matching

Ghannadi¹,

Saadatseresht²,

Izadi

et al. 2020

IET Radar, Sonar & Navigation

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“…The method uses phase congruency [21] to eliminate the outliers. The spatial correlation strategy based on stationary wavelet transform is utilised in [22] to select reliable keypoints from the SIFT algorithm to reduce the influence of speckle noise. In [23], an improved anisotropic Gaussian scale‐space SIFT is used to find the keypoints for SAR image registration.…”

Section: Introductionmentioning

confidence: 99%

Structure tensor‐based SIFT algorithm for SAR image registration

Divya

Paul

Pati

2020

IET Image Processing

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“…It is a critical prerequisite of the tasks of analyzing two or more relevant images. After the first full digit image registration was applied in remote sensing field [2], image registration becomes popular in remote sensing, which can be applied for environmental monitoring, change detection, image mosaic, and so on [3], [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Feature Matching for Remote Sensing Image Registration via Manifold Regularization

Zhou

Dai

Tian

et al. 2020

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

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Feature matching is critical in remote sensing images analyzing, aiming to find the optimal mapping between correspondences. Regularization technology is essential to ensure the well-posedness of feature matching. However, current regularization-based methods scarcely consider the geometry structure of the image, which is beneficial for estimating the mapping, especially when the image pairs have a large view or scale change and local distortion. In this paper, we introduce manifold regularization to overcome this limit and formulate feature matching as a unified semi-supervised latent variable mixture model for both rigid and nonrigid transformations. Specially, we apply a Bayesian model with latent variables indicating whether matches in the putative correspondences are outliers or inliers. Moreover, we employ all the feature points, only part of which have correct matches, to express the intrinsic structure, which is preserved by manifold regularization. Finally, we combine manifold regularization with three different transformation models (e.g., rigid, affine, and TPS (Thin-plate spline)) to estimate the corresponding mappings. Experimental results on four remote sensing image datasets demonstrate that our method can significantly outperform the state-of-the-art.

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SAR Image Registration Using Multiscale Image Patch Features With Sparse Representation

Cited by 33 publications

References 25 publications

Optimal texture image reconstruction method for improvement of SAR image matching

Optimal texture image reconstruction method for improvement of SAR image matching

Structure tensor‐based SIFT algorithm for SAR image registration

Feature Matching for Remote Sensing Image Registration via Manifold Regularization

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