A novel SAR and optical image fusion algorithm based on an improved SPCNN and phase congruency information

Fu, Yukai; Yang, Shuwen; Li, Yikun; Yan, Heng; Zheng, Yi

doi:10.1080/01431161.2023.2179899

Cited by 8 publications

(2 citation statements)

References 52 publications

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“…IMAGE fusion [1], 3D reconstruction [2], and target recognition [3]. Nowadays, research generally focuses on high-quality remote-sensing images with relatively simple background information.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Multisource images acquired using different platforms and sensors for Earth observation can acquire different characteristics and information regarding target ground objects, and the integrated use of multisource remote sensing data can achieve complementary advantages, which can help improve the interpretation accuracy of target ground objects and obtain rich and complete comprehensive information. [1][2][3][4] Image matching is the basis for the good integration of multisource images, and its accuracy significantly impacts subsequent applications. 5 In recent years, researchers have extensively studied multisource remote sensing image matching.…”

Section: Introductionmentioning

confidence: 99%

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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Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

Yang,

Wang,

Yuan

et al. 2024

Science of The Total Environment

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A novel SAR and optical image fusion algorithm based on an improved SPCNN and phase congruency information

Cited by 8 publications

References 52 publications

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

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

Multisource remote sensing image matching based on expanded phase consistency

Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

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