Prof. Yongxin Liu Profile

Prof. Yongxin Liu

at Inner Mongolia Univ

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Publications (2)

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SPIE Journal Paper | 6 May 2022

Interference image registration combined by enhanced scale-invariant feature transform characteristics and correlation coefficient

Zhaoxia Wang, Yongxin Liu, Jie Zhang, Chenqing Fan, Hui Zhang

JARS, Vol. 16, Issue 02, 026508, (May 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.026508

KEYWORDS: Image registration, Image enhancement, Interferometry, Antennas, Computer simulations, Phase interferometry, Image restoration, Image processing, Feature extraction, Image transmission

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To realize the digital elevation inversion of the interferometric imaging radar altimeter (InIRA), an interference complex images registration algorithm combining enhanced scale-invariant feature transform (SIFT) characteristics with correlation coefficient is proposed. First, the locally tuned nonlinear method is used to enhance the image features. Then, SIFT algorithm is used to extract the matched feature points that are used as control points after screening. Based on these control points, the affine transformation is applied to calculate the coarse matching relation. Second, multiple control points are chosen uniformly. The local accurate offsets are determined by interpolating and calculating the maximum of correlation coefficients. The least-squares method is used to fit the difference between the two images. Third, the two images are matched by interpolating and resampling the one to be registered. Finally, the simulated InIRA sea surface images and the Sentinel-1A images of the Mount Hua area are employed to experiment. The results show that the proposed algorithm combines the advantages of SIFT algorithm and correlation coefficient algorithm. It is robust and its registration accuracy is better than the particle swarm optimization sample consensus algorithm, unsupervised deep-learning algorithm, SIFT algorithm, and correlation coefficient algorithm.

Proceedings Article | 12 March 2021 Paper

Improved global optimal association algorithm for ships track

Linglin Wang, Yongxin Liu

Proceedings Volume 11763, 117632V (2021) https://doi.org/10.1117/12.2586421

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For solving the problem of ship track association using data from High Frequency Surface Wave Radar (HFSWR) and Automatic Identification System (AIS), an improved global optimal algorithm is proposed in this paper. In order to increase association tracks from HFSWR more, a HFSWR tracks error correction method is used first. Then the associable tracks set is formed by initial match, which data from these two sensors associated using three parameters. Through these steps, the amount of computation in global optimization algorithm can be reduced, and this also made the algorithm more effective. Furthermore, the one to one track is associated accurately by using global optimal algorithm to associate tracks from HFSWR and AIS. Finally measured data in October 31, 2011 are employed to compare with nearest neighbor (NN) track association algorithm and this improved global optimal algorithm. The result shows that improved global optimal algorithm track association get more associations, and it has better stability than nearest neighbor algorithm especially in more complex situations.

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