High-Performance SAR Image Matching Using Improved SIFT Framework Based on Rolling Guidance Filter and ROEWA-Powered Feature

Yu, Qiuze; Zhou, Shaosheng; Jiang, Yuxuan; Wu, Peng; Xu, Yong

doi:10.1109/jstars.2019.2897171

Cited by 29 publications

(20 citation statements)

References 30 publications

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“…SIFT features are based on the extrinsic view of the object of interest, independent of the size and rotation of the image [23][24]. It also has a high tolerance for light, noise, and micro-angle changes.…”

Section: B Sift Descriptionmentioning

confidence: 99%

“…The principle of uniqueness constraint: a feature point in the left image, if a matching point exists in the right image, it is unique. Combining formulas (22), (24) and uniqueness constraints, the matching area will be further reduced. Obviously, this greatly reduces the area of the target point matching search.…”

Section: Matching Of Fuzzy Feature Points In Video Images Of Mobilmentioning

confidence: 99%

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Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

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Extracting the fuzzy feature of the mobile video image can effectively improve the low illumination image quality. Traditional methods are used to construct fuzzy feature indexes of mobile terminal video images, and the detailed information of video images is divided, but the bidirectional matching of feature points is ignored, which leads to low extraction accuracy. Therefore, this paper proposes a method for extracting fuzzy features of mobile terminal video images based on SURF-based virtual reality technology. First, perform video image grayscale extraction on the input mobile terminal video image, and detect the closed area in the mobile terminal video image as the radiation invariant area of the terminal video image. Secondly, Hessian matrix is used to detect the feature points of the image, and the non-maximum suppression method and interpolation operation are used to find and locate the extreme value points. Then, the main direction of feature points was determined, and SURF description operator was used for matching to obtain initial matching point pairs. Finally, the obtained fuzzy feature one-way matching result of the video image is matched in two directions, the closest distance ratio is used to match the feature points, and the full constraint condition is used to filter out the wrong matching point pairs, thereby completing the mobile terminal video image fuzzy feature extraction. The experimental results show that the proposed algorithm is effective in feature extraction and matching, stability and speed. The misrecognition rate of the algorithm in this paper is 0.101, and the time used is only 0.41 s, which fully meets the real-time requirements.

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Section: B Sift Descriptionmentioning

confidence: 99%

Section: Matching Of Fuzzy Feature Points In Video Images Of Mobilmentioning

confidence: 99%

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng

2020

IEEE Access

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“…Beside ABM methods which are usually applied for generating DEM or producing land deformation measurements, FBM methods are also used and are mostly applied in the registration process. Among FBM algorithms, scale‐invariant feature transform (SIFT) [1, 7, 18–23] and speeded‐up robust features (SURFs) [18] are the most common methods. Some geometrical constraints, such as epipolar geometry, can be considered in improving the precision and efficiency of the process [24].…”

Section: Introductionmentioning

confidence: 99%

Optimal texture image reconstruction method for improvement of SAR image matching

Ghannadi¹,

Saadatseresht²,

Izadi

et al. 2020

IET Radar, Sonar & Navigation

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“…The technique possesses the merits of high computational efficiency, high theoretical accuracy, and insensitivity to geometric deformations and differences [ 7 ]. As a result, feature-based image matching has received a lot of attention in the field of computer vision [ 8 , 9 , 10 , 11 ], photogrammetry and remote sensing [ 12 , 13 , 14 , 15 ], in applications [ 16 , 17 , 18 ] such as multiple view 3D reconstruction, remote sensing image fusion and visual localization.…”

Section: Introductionmentioning

confidence: 99%

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Liu

Jia

et al. 2020

Sensors

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Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The quality of image matching is heavily dependent on image details and naturalness. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.

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High-Performance SAR Image Matching Using Improved SIFT Framework Based on Rolling Guidance Filter and ROEWA-Powered Feature

Cited by 29 publications

References 30 publications

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Optimal texture image reconstruction method for improvement of SAR image matching

A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

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