LPSO: Multi-Source Image Matching Considering the Description of Local Phase Sharpness Orientation

Yang, Wei; Xu, Chuan; Mei, Liye; Yao, Yongxiang; Chang, Liu

doi:10.1109/jphot.2022.3144227

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…However, it does not preserve the features of low-frequency regions of an image. A previous study 47 used a second-order gradient to enrich the image detail information with good results. However, the gradient information at a single pixel point does not fully reflect the local structural characteristics of an image.…”

Section: Multisource Remote Sensing Image Matching Methods Based On E...mentioning

confidence: 99%

“…Four state-of-the-art methods, log-Gabor histogram descriptor (LGHD), 55 RIFT, 26 histogram of absolute phase consistency gradients (HAPCG), 41 and local phase sharpness orientation (LPSO), 47 are selected for comparison with the proposed method, and the parameters of the four comparison methods are adopted from the original paper. All the above methods are implemented under Matlab R2016a, and the experimental platform processor is AMD Ryzen 5 4600H CPU @ 3.00 GHz, RAM is 16 GB, and Windows 11 X64 operating system.…”

Section: Methodsmentioning

confidence: 99%

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Multisource remote sensing image matching based on expanded phase consistency

Zheng,

Yang,

et al. 2023

J. Appl. Rem. Sens.

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.To better cope with significant nonlinear radiation distortions and severe geometric distortions in multisource remote sensing image matching, we propose a high-precision image matching method for multisource remote sensing images based on extended phase consistency (EPC). First, feature detection was performed based on an anisotropic weighted moment equation. We used the EPC model to construct multi-orientation weighted phase sharpness map and the multiscale phase orientation index map and built a gradient location orientation histogram-like local feature descriptor to obtain the corresponding features based on the feature maps. The spatial relationship constraint function was introduced to screen the obtained initial matches to reduce the negative effects caused by the high proportion of outliers. The remaining outliers were removed using the marginalizing sample consensus (MAGSAC) method, and the transformation parameters of the sensed image were calculated based on the iteratively optimized homography matrix model. Based on the feature detection results obtained from two distinct groups of synthetic data, it is evident that the proposed detector outperforms the three classical detectors in terms of repeatability and uniformity. The registration performance experiment data were divided into six groups, and the proposed method was compared with the log-Gabor histogram descriptor, radiation-variation insensitive feature transform, histogram of absolute phase consistency gradients, and local phase sharpness orientation methods. The experimental results showed that the proposed method is significantly better than the existing algorithms in terms of image matching performance and achieves high registration accuracy.

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Section: Multisource Remote Sensing Image Matching Methods Based On E...mentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Multisource remote sensing image matching based on expanded phase consistency

Zheng,

Yang,

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…However, it does not preserve the features of low-frequency regions of an image. [39] used a second-order gradient to enrich the image detail information with good results. However, the gradient information at a single pixel point does not fully reflect the local structural characteristics of an image.…”

Section: Multiscale Phase Weighted Energy Convolution Feature Descrip...mentioning

confidence: 99%

A Heterogeneous Remote Sensing Image Matching Method for Urban Areas With Complex Terrain Based on 3D Spatial Relationship Constraints

Zheng,

Yang,

et al. 2024

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

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Heterogeneous high-resolution remote sensing image matching will be disturbed by the differences in sensor type, imaging angle, height, and imaging time, and the matching difficulty is further increased in complex scenes with dense urban buildings and noticeable height differences. This paper proposes a method for matching heterogeneous high-resolution remote sensing images based on partitioned feature extraction and threedimensional spatial constraints. First, this paper conducts image partitioning based on the geometric differences of ground objects. Two feature extraction methods, namely, adaptive phase threshold and weighted moment map, are employed to extract feature points independently. To address the issue of inaccurate feature descriptions caused by drastic changes in viewing angles in buildings, we construct a robust feature descriptor by combining a multi-scale phase weighted energy convolution histogram (MSPW-ECH) with a new gradient location orientation histogram (GLOH)-like local feature descriptor. Additionally, a new similarity measure incorporating three-dimensional spatial constraints and the Marginalizing Sample Consensus (MAGSAC) method is applied to eliminate mismatched point pairs, ensuring the acquisition of precise matching points. Based on the feature detection results of two different synthetic data sets, it is evident that the proposed detector outperforms the three classical detectors in terms of repeatability and uniformity. Ultimately, the matching performance is experimentally verified on six groups of heterogeneous high-resolution remote sensing images. The experimental results show that the proposed method significantly outperforms RIFT, HAPCG, and MS-HLMO methods and achieves the best matching accuracy results.

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“…The proposed method is compared with several baseline methods, including RIFT (2019) [11], LPSO (2022) [51] LNIFT (2022) [4] and HOWP (2023) [29]. The parameters of compared methods are tuned as the same.…”

Section: A Datasets and Experimental Settingsmentioning

confidence: 99%

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

Huang,

Wan,

Zhou

et al. 2024

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

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Matching planetary remote sensing images (PRSI) is essential for deep space exploration. Through matching optical images collected by different probes, the terrain of planets can be accurately mapped; however, PRSI lacks surface texture information and exhibits noticeable nonlinear radiation differences, e.g., illumination differences. It is not easy to achieve satisfactory results through traditional matching methods. In order to cope with the above problems, a new PRSI matching method has been proposed. It can extract low-high frequency features by building doublefrequency scale space. In addition, we also use the nonmaximum suppression strategy for rejecting overlapped feature points, which reduces the time consumption and improves the matching accuracy. The experimental results show that the proposed method can effectively match PRSI and be superior to comparable methods.

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LPSO: Multi-Source Image Matching Considering the Description of Local Phase Sharpness Orientation

Cited by 11 publications

References 22 publications

Multisource remote sensing image matching based on expanded phase consistency

Multisource remote sensing image matching based on expanded phase consistency

A Heterogeneous Remote Sensing Image Matching Method for Urban Areas With Complex Terrain Based on 3D Spatial Relationship Constraints

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

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