A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images
To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated rad...
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/15/3600 |
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| author | Yanbo Wei Yalin Liu Yifei Lei Ruiyao Lian Zhizhong Lu Lei Sun |
| author_facet | Yanbo Wei Yalin Liu Yifei Lei Ruiyao Lian Zhizhong Lu Lei Sun |
| author_sort | Yanbo Wei |
| collection | DOAJ |
| description | To control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated radar image and the rain-free radar image is utilized to detect rainfall. However, only the correlation coefficient at a position in the lagged azimuth is utilized, and a statistical hard threshold is adopted. By deeply investigating the difference between the calculated correlation characteristic and the marine radar images, the correlation coefficient in the lagged azimuth can be used to constitute the correlation coefficient feature vector (CCFV). Then, an unsupervised K-means clustering learning method is used to obtain the clustering centers. Based on the constituted CCFV and the K-means clustering algorithm, a new method of rainfall detection from the collected X-band marine radar images is proposed. The acquired X-band marine radar images are utilized to verify the effectiveness of the proposed rainfall detection method. Compared with the zero-pixel percentage (ZPP) method, the correlation coefficient difference (CCD) method, the support vector machine (SVM) method and the wave texture difference (WTD) method, the experimental results demonstrate that the proposed method could finish the task of rainfall detection, and the detection accuracy increases by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10.0</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.0</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.6</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively, for the proportion of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>25</mn><mo>%</mo></mrow></semantics></math></inline-formula> training dataset. |
| first_indexed | 2024-03-09T05:02:56Z |
| format | Article |
| id | doaj.art-72ba0d9b38874a128317d096d2b50ba3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:02:56Z |
| publishDate | 2022-07-01 |
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| spelling | doaj.art-72ba0d9b38874a128317d096d2b50ba32023-12-03T12:58:02ZengMDPI AGRemote Sensing2072-42922022-07-011415360010.3390/rs14153600A New Method of Rainfall Detection from the Collected X-Band Marine Radar ImagesYanbo Wei0Yalin Liu1Yifei Lei2Ruiyao Lian3Zhizhong Lu4Lei Sun5College of Physics and Electronic Information, Luoyang Normal University, No. 6 Jiqing Road, Luoyang 471934, ChinaSchool of Electrical Engineering, Zhengzhou Railway Vocational and Technical College, No. 56 Pengcheng Avenue, Zhengzhou 451460, ChinaCollege of Physics and Electronic Information, Luoyang Normal University, No. 6 Jiqing Road, Luoyang 471934, ChinaCollege of Physics and Electronic Information, Luoyang Normal University, No. 6 Jiqing Road, Luoyang 471934, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, ChinaTo control the quality of X-band marine radar images for retrieving information and improve the inversion accuracy, the research on rainfall detection from marine radar images is investigated in this paper. Currently, the difference in the correlation characteristic between the rain-contaminated radar image and the rain-free radar image is utilized to detect rainfall. However, only the correlation coefficient at a position in the lagged azimuth is utilized, and a statistical hard threshold is adopted. By deeply investigating the difference between the calculated correlation characteristic and the marine radar images, the correlation coefficient in the lagged azimuth can be used to constitute the correlation coefficient feature vector (CCFV). Then, an unsupervised K-means clustering learning method is used to obtain the clustering centers. Based on the constituted CCFV and the K-means clustering algorithm, a new method of rainfall detection from the collected X-band marine radar images is proposed. The acquired X-band marine radar images are utilized to verify the effectiveness of the proposed rainfall detection method. Compared with the zero-pixel percentage (ZPP) method, the correlation coefficient difference (CCD) method, the support vector machine (SVM) method and the wave texture difference (WTD) method, the experimental results demonstrate that the proposed method could finish the task of rainfall detection, and the detection accuracy increases by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10.0</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.0</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.6</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively, for the proportion of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>25</mn><mo>%</mo></mrow></semantics></math></inline-formula> training dataset.https://www.mdpi.com/2072-4292/14/15/3600correlation coefficient feature vector (CCFV)K-means clustering algorithmmarine radar imagesrainfall detection |
| spellingShingle | Yanbo Wei Yalin Liu Yifei Lei Ruiyao Lian Zhizhong Lu Lei Sun A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images Remote Sensing correlation coefficient feature vector (CCFV) K-means clustering algorithm marine radar images rainfall detection |
| title | A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images |
| title_full | A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images |
| title_fullStr | A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images |
| title_full_unstemmed | A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images |
| title_short | A New Method of Rainfall Detection from the Collected X-Band Marine Radar Images |
| title_sort | new method of rainfall detection from the collected x band marine radar images |
| topic | correlation coefficient feature vector (CCFV) K-means clustering algorithm marine radar images rainfall detection |
| url | https://www.mdpi.com/2072-4292/14/15/3600 |
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