A new affine invariant descriptor framework in shearlets domain for SAR image multiscale registration

Liu, Xiangzeng; Tian, Zheng; Lü, Qiang; Yang, Liang; Chai, Chunyan

doi:10.1016/j.aeue.2013.03.002

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…Shearlets have begun to be applied in the field of image processing, including image denoising [34], SIFT-based image registration [35], image in painting [36], and image fusion [37]. The relatively simple numerical implementation of shearlets suggests their use over other anisotropic systems, which suffer from more complicated implementations.…”

Section: B Background On Shearletsmentioning

confidence: 99%

Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features

Murphy

Moigne

Harding

2016

IEEE Trans. Geosci. Remote Sensing

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Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets.Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone.

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Section: B Background On Shearletsmentioning

confidence: 99%

Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features

Murphy

Moigne

Harding

2016

IEEE Trans. Geosci. Remote Sensing

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“…Besides, harmonic analysis transforms have begun to be applied to the filed of SIFT-based feature point matching methods [19]. For example, kernel affine invariant SIFT (KA-SIFT) [25] matches the feature points detected from the different sub-images in the corresponding layer, which are obtained by the shearlet decomposition and affine-SIFT (ASIFT) algorithm [26]. Instead of building gradient histograms as SIFT-like descriptors, speeded-up robust feature (SURF) [27] utilities the sums of Haar wavelet responses, and estimates the principal orientations within a sliding orientation window.…”

Section: A Feature Point Matching Based On Local Feature Similaritymentioning

confidence: 99%

RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration

Zhao¹,

An²,

Wu³

et al. 2022

Preprint

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Reliable feature point matching is a vital yet challenging process in feature-based image registration. In this paper, a robust feature point matching algorithm called Recovery and Filtering Vertex Trichotomy Matching (RFVTM) is proposed to remove outliers and retain sufficient inliers for remote sensing images. A novel affine invariant descriptor called vertex trichotomy descriptor is proposed on the basis of that geometrical relations between any of vertices and lines are preserved after affine transformations, which is constructed by mapping each vertex into trichotomy sets. The outlier removals in Vertex Trichotomy Matching (VTM) are implemented by iteratively comparing the disparity of corresponding vertex trichotomy descriptors. Some inliers mistakenly validated by a large amount of outliers are removed in VTM iterations, and several residual outliers close to correct locations cannot be excluded with the same graph structures. Therefore, a recovery and filtering strategy is designed to recover some inliers based on identical vertex trichotomy descriptors and restricted transformation errors. Assisted with the additional recovered inliers, residual outliers can also be filtered out during the process of reaching identical graph for the expanded vertex sets. Experimental results demonstrate the superior performance on precision and stability of this algorithm under various conditions, such as remote sensing images with large transformations, duplicated patterns, or inconsistent spectral content.Index terms-Feature point matching, graph based matching, image registration, remote sensing, Vertex Trichotomy. I. INTRODUCTIONImage registration is a crucial preprocessing technology for image analysis, which has been widely applied in remote sensing, computer vision, medical imaging, and map search [1], [2]. Regarding remote sensing applications in particular, image registration employed in geosciences is frequently used in a wide variety of disciplines, such as volcanology [3][4][5][6], structural geology and paleoseismology [7-9], and soil

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“…BFSIFT (Wang, You, and Fu 2012) chooses to use a bilateral filter (BF) to construct the anisotropic scale space of the SAR image. Kernel affine invariant SIFT (KA-SIFT) (Liu et al 2013) is an affine invariant descriptor framework in shearlet domain, which is based on SIFT and kernel space theory. Image registration is implemented from the coarsest level to the finest by gradually optimizing transformation parameters at a series of increasingly finer spatial resolutions.…”

Section: Introductionmentioning

confidence: 99%

A new affine invariant feature extraction method for SAR image registration

Yang

Tian

Zhao

2014

International Journal of Remote Sensing

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To cope with the problem of automatic registration of synthetic aperture radar (SAR) images, a novel affine invariant feature extraction method is proposed. The proposed method adapts the salient image disks (SIDs) extraction method to accurately localize affine invariant features in SAR images. The contributions mainly include three aspects. First, a multi-scale isotropic matched filter (MIMF) operator is modified to detect salient features. Then, a multi-resolution search strategy is adopted for eliminating unstable features, and affine invariance is achieved by an affine adaptation process. Finally, a point-set registration method based on Coherent Point Drift (CPD) is applied to the SAR image registration problem. Experiment results show that the proposed method has great improvement compared with the original method.

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A new affine invariant descriptor framework in shearlets domain for SAR image multiscale registration

Cited by 7 publications

References 32 publications

Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features

Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features

RFVTM: A Recovery and Filtering Vertex Trichotomy Matching for Remote Sensing Image Registration

A new affine invariant feature extraction method for SAR image registration

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