Remote Sensing Image Registration Based on Phase Congruency Feature Detection and Spatial Constraint Matching

Ma, Wenping; Wu, Yue; Liu, Shaodi; Su, Qingxiu; Zhong, Yong

doi:10.1109/access.2018.2883410

Cited by 41 publications

(21 citation statements)

References 39 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%

“…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. Ma et al [27] combine the frequency domain (PC) and the spatial domain (SAR-SIFT operator) to detect image features. The extracted features are robust because it depends on the image structure.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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“…To address this problem, a corner and edge detector have been developed from the phase congruency model of feature detection, which provides information invariant to image contrast [24]. Recently, many researchers have used phase congruency methods for feature detection [54,55,56,57].…”

Section: Related Workmentioning

confidence: 99%

Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Liu

Pan

2019

Sensors

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Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.

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“…Fan et al [24] combined an advanced corner detection method and phase congruency structural descriptor to match optical and SAR images. Ma et al [25] combined feature detection methods both in the frequency domain (phase congruency) and the spatial domain to improve the registration accuracy. Paul and Pati [26] improved the repeatability rate of the extracted features and utilized a structure descriptor to handle significant intensity differences and noise effects.…”

Section: Introductionmentioning

confidence: 99%

Automatic Registration of Optical and SAR Images Via Improved Phase Congruency Model

Xiang

Tao

Wang

et al. 2020

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

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In this paper, we propose an automatic and efficient method to solve optical and SAR image registration using the improved phase congruency (PC) model. First, evenly-distributed keypoints are extracted from the optical images via the block harris method. Complementary grid points are then selected in image regions with poor structural information and supplemented to the keypoint set. For each keypoint, a robust feature representation that captures the local spatial relationship is proposed based on the improved PC model. Specifically, we propose to use two different PC models, the classic PC and the SAR-PC, to construct features for optical and SAR images respectively. The PC features of several directions are aggregated to construct the feature descriptors, and a similarity metric via the phase correlation of feature descriptors is obtained. The proposed similarity metric can not only find accurate correspondence but also present efficient results without presetting the size of the search region. We compare the proposed method with two baselines and stateof-the-art (SOTA) methods, i.e. OS-SIFT, HOPC, CFOG, in various scenarios. The results show that the proposed method outperforms the baselines and shows comparable performance with SOTA methods in regions with abundant structural information and better performance in regions with less structural information. Moreover, we build a high-resolution optical and SAR image matching dataset, which consists of 10,692 nonoverlapping patch-pairs of 256 × 256 pixels and 1-m resolution. Results of two benchmarks, Siamese deep matching network and conditional Generative Adversarial Networks, show that this dataset is practical and challenging.

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Remote Sensing Image Registration Based on Phase Congruency Feature Detection and Spatial Constraint Matching

Cited by 41 publications

References 39 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

Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images

Automatic Registration of Optical and SAR Images Via Improved Phase Congruency Model

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