Shiyu Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shiyu Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Haiyang Qiu
School of Naval Architecture and Ocean Engineering, Guangzhou Maritime University, Guangzhou 510725, China
1 shared publication - Hui Wang
School of Naval Architecture and Ocean Engineering, Guangzhou Maritime University, Guangzhou 510725, China
1 shared publication - Huilin Ge
Jiangsu University of Science and Technology
1 shared publication - Yanbo Wei
College of Electrical Engineering and Automation, Luoyang Normal University, No. 6 Jiqing Road, Luoyang 471934, China
1 shared publication - Zhiyu Zhu
Automation Academy, Jiangsu University of Science and Technology, Zhenjiang, China
1 shared publication - Zhizhong Lu
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
1 shared publication
Latest Publications
- Open Access
- 14 April 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (8) , 2078
Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images
The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.
- 14 April 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (8) , 2078
Open AccessDevelopment of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images
The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.