A local phase based invariant feature for remote sensing image matching

Ye, Yuanxin; Shan, Jie; Hao, Siyuan; Bruzzone, Lorenzo; Qin, Yao

doi:10.1016/j.isprsjprs.2018.06.010

Cited by 81 publications

(43 citation statements)

References 43 publications

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“…However, these detectors usually have difficulty in detecting highly repeatable feature points between multispectral images for the difference of gradient, which substantially degrades the matching performance [24]. Compared with the image gradient, PC model is more robust to changes in illumination and contrast, many researchers have used PC detector [25]- [27] for feature detection. Ye et al [25] proposed a feature detector (MMPC-Lap) and a feature descriptor named local histogram of orientated phase congruency (LHOPC) for remote sensing image matching, which is invariant to illumination and contrast variation.…”

Section: Related Workmentioning

confidence: 99%

“…The gray distortion caused by the nonlinear intensity difference is a great challenge to the multispectral image matching. Through research on image matching, some researchers found that the geometric structure information between multispectral images is more stable than gradients or intensities information under nonlinear intensity variation in multispectral images [20], [24], [25]. Based on this observation, feature points can be detected based on the structure consistency of image, which can be evaluated by calculating similarity metrics on structure or shape descriptors.…”

Section: Introductionmentioning

confidence: 99%

“…Phase congruency (PC) model has been demonstrated to captures the structural information consistent with human visual and is robust to illumination and contrast differences [26]. Therefore, some algorithms based on image phase congruency are applied to multispectral or multimodal image matching [11], [25]- [28]. However, PC has limits on itself as [26], [29]: (1) Phase congruency is highly affected by noise since it mainly contains edges structural.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Matching of Multispectral Images Based on Nonlinear Diffusion of Image Structures

Ruixiang

Zhao

Zhang

et al. 2021

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

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Matching of Multispectral Images Based on Nonlinear Diffusion of Image Structures

Ruixiang

Zhao

Zhang

et al. 2021

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

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“…The system accuracy is computed at equal error rate (EER) value which reflects the maximum system performance when the false acceptance rate (FAR) equals to the false rejection rate (FRR) as explained in 15. The error distance between x ̅ and y ̅ with m+1 length can be described in (16)(17)(18) [30][31][32][33][34].…”

Section: Classificationmentioning

confidence: 99%

Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

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Phase congruency is an edge detector and measurement of the significant feature in the image. It is a robust method against contrast and illumination variation. In this paper, two novel techniques are introduced for developing a low-cost human identification system based on face recognition. Firstly, the valuable phase congruency features, the gradient-edges and their associated angles are utilized separately for classifying 130 subjects taken from three face databases with the motivation of eliminating the feature extraction phase. By doing this, the complexity can be significantly reduced. Secondly, the training process is modified when a new technique, called averaging-vectors is developed to accelerate the training process and minimizes the matching time to the lowest value. However, for more comparison and accurate evaluation, three competitive classifiers: Euclidean distance (ED), cosine distance (CD), and Manhattan distance (MD) are considered in this work. The system performance is very competitive and acceptable, where the experimental results show promising recognition rates with a reasonable matching time.

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“…Fortunately, because of the stability of the satellite platform and geolocation tools, the affine transformation model is able local geometric distortions [21] and the histogram of orientated phase congruency (HOPC) descriptor was developed to address the nonlinear radiometric differences [22]. To address the poor distribution of detected features, novel score criteria for the feature points, a feature detection method based on phase congruency, and the uniform partitioning strategy were utilized to generate an adequate number of high-quality, uniformly-distributed point features in the spatial and scale spaces, such as UR-SIFT [23], UC-SIFT [24], and MMPC-Lap [25]. For feature matching, a support-line descriptor based on multiple adaptive binning gradient histograms was developed to filter out the outliers after the initial matching [26] to produce more high-precision correspondences.…”

Section: Introductionmentioning

confidence: 99%

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

et al. 2018

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Image registration is a core technology of many different image processing areas and is widely used in the remote sensing community. The accuracy of image registration largely determines the effect of subsequent applications. In recent years, phase correlation-based image registration has drawn much attention because of its high accuracy and efficiency as well as its robustness to gray difference and even slight changes in content. Many researchers have reported that the phase correlation method can acquire a sub-pixel accuracy of 1/10 or even 1/100. However, its performance is acquired only in the case of translation, which limits the scope of the application of the method. However, there are few reports on the estimation of scales and angles based on the phase correlation method. To take advantage of the high accuracy property and other merits of phase correlation-based image registration and extend it to estimate the similarity transform, we proposed a novel algorithm, the Multilayer Polar Fourier Transform (MPFT), which uses a fast and accurate polar Fourier transform with different scaling factors to calculate the log-polar Fourier transform. The structure of the polar grids of MPFT is more similar to the one of the log-polar grid. In particular, for rotation estimation only, the polar grid of MPFT is the calculation grid. To validate its effectiveness and high accuracy in estimating angles and scales, both qualitative and quantitative experiments were carried out. The quantitative experiments included a numerical simulation as well as synthetic and real data experiments. The experimental results showed that the proposed method, MPFT, performs better than the existing phase correlation-based similarity transform estimation methods, the Pseudo-polar Fourier Transform (PPFT) and the Multilayer Fractional Fourier Transform method (MLFFT), and the classical feature-based registration method, Scale-Invariant Feature Transform (SIFT), and its variant, ms-SIFT.

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A local phase based invariant feature for remote sensing image matching

Cited by 81 publications

References 43 publications

Automatic Matching of Multispectral Images Based on Nonlinear Diffusion of Image Structures

Automatic Matching of Multispectral Images Based on Nonlinear Diffusion of Image Structures

Towards better performance: phase congruency based face recognition

An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform

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