A Method for Detecting Rainfall From X-Band Marine Radar Images

Yan, Zheng; Shi, Z.; Lu, Zhizhong; Ma, Wenfeng

doi:10.1109/access.2020.2968601

Cited by 12 publications

(7 citation statements)

References 21 publications

(21 reference statements)

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“…Then, the modified Laplace linear template is adopted to locate the interference noise points, and the detected noise points are repaired by the segmental interpolation method to filter out the noise. Since the random superposition interference of various echo noises such as fixed objects and ships exists in the whole radar image, which affects the accuracy of rainfall detection, it is necessary to select the appropriate area in the radar image for rainfall detection [25].…”

Section: Cochannel Interference Suppressionmentioning

confidence: 99%

“…Based on the sea clutter analysis method proposed by Dong and Merrett [24], the correlation coefficient difference (CCD) method is proposed to detect rainfall from X-band marine radar images, and the detection threshold is set to 1/e. Since the correlation coefficient in the azimuth direction between light rain and heavy rain has little difference, and a correlation difference fluctuation exists in the experiment, the performance of the CCD method needs to be further studied [25]. In [26], an unsupervised clustering-based method is proposed to identify lowbackscatter and rain-contaminated regions in X-band marine radar images.…”

Section: Introductionmentioning

confidence: 99%

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A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar

Sun

Wang

et al. 2022

Journal of Sensors

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To suppress the influence of rainfall when extracting sea surface wind and wave parameters using X-band marine radar and control the quality of the collected radar image, it is necessary to detect whether the radar image is contaminated by rainfall. Since the detection accuracy of the statistical characteristics methods (e.g., the zero pixel percentage method and the high-clutter direction method) is limited and the threshold is difficult to determine, the machine learning methods (e.g., the support vector machine-based method and the neural network algorithm) are difficult to select appropriate quality and quantity of data for model training. Therefore, based on the feature that rainfall can change the sea surface texture, a wave texture difference method for rainfall detection is proposed in this paper. Considering the spatial rainfall is uneven, the polar coordinates of the radar image are converted into Cartesian coordinates to detect rainfall. To express the maximum wave difference more accurately, the calculation method of the pixels in the radar texture difference map is redefined. Then, a consecutive pixel method is used to detect the calculated radar texture difference map, and this method can detect adaptively with the change of wind. The data collected from the shore of Haitan Island along the East China Sea are used to validate the effectiveness of the proposed method. Compared with the zero pixel percentage method and the support vector machine-based method, the experimental results demonstrate that the proposed method has better rainfall detection performance. In addition, the research on the applicability of the proposed method shows that the wave texture difference method can finish the task of rainfall detection in most marine environments.

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Section: Cochannel Interference Suppressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar

Sun

Wang

et al. 2022

Journal of Sensors

Self Cite

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“…In Ref. [22], a threshold of the thermal noise is proposed for rainfall detection in the same radar system as in this article. When the µ of the wave area is less than 0.3 V, the radar image is determined as the rain-free image of low sea conditions.…”

Section: Selected Radar Imagementioning

confidence: 99%

“…In Ref. [22], a novel rainfall detection method based on the difference in the correlation characteristics is presented. The correlation coefficient of the X-band marine radar images has little difference in the same lagged azimuth when the weather is light rain and heavy rain, and the error exists in the determination of the threshold due to the existence of correlation difference fluctuation in the experiment.…”

Section: Introductionmentioning

confidence: 99%

A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images

Lu¹,

Sun²,

Zhou³

2021

Applied Sciences

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Currently, it is a hot research topic to retrieve the wave parameters by using X-band marine radar. However, the rainfall noise usually exists in the collected marine radar images, which seriously interferes with the extraction of the wave parameters. To reduce the influence of rainfall noise, the zero-pixel percentage (ZPP) method is widely used to detect rainfall in radar images, but the detection accuracy is limited, and the selection of the threshold needs to be further studied. Based on the ZPP method, the ratio of zero intensity to echo (RZE) method for rainfall detection is proposed in this paper. The detection threshold is determined by statistical analysis of a large amount of radar data. Additionally, it is proposed for the first time to retrieve the rainfall intensity level from X-band marine radar images. In addition, the concept of the occlusion area is proposed. The proposed area and the wave area are used as the rainfall detection area of the radar image, respectively, for experimental research. The data obtained from the Pingtan experimental base in Fujian Province are used to verify the effectiveness of the proposed method. The experimental results show that the detection accuracy of the proposed method is 11.7% higher than that of the ZPP method, and the accuracy of rainfall intensity level retrieval is 84%.

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“…Relying on the advantages of detection in low-visibility conditions, marine radar is regarded as one of the key instruments in the future of ocean monitoring, pilotage and collision avoidance [ 1 ]. Especially in terms of ocean information extraction, if reliable winds [ 2 ], waves [ 3 ], currents [ 4 ], tides [ 5 ], sea ice [ 6 ] or oil spill [ 7 ] measurements can be obtained from marine radars, the costs associated with traditional field means such as anemometers, buoys or manual visual inspection can be significantly reduced [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Median Filtering Algorithm for Radar Image Co-Channel Interference Suppression

Liu

2022

Sensors

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In order to increase the accuracy of ocean monitoring, this paper proposes an improved adaptive median filtering algorithm based on the tangential interference ratio to better suppress marine radar co-channel interference. To solve the problem that co-channel interference reduces the accuracy of radar images’ parameter extraction, this paper constructs a tangential interference ratio model based on the improved Laplace operator, which is used to describe the ratio of co-channel interference along the antenna rotation direction in the original radar image. Based on the idea of between-class variance, the tangential interference ratio threshold is selected to divide co-channel interference into high-ratio regions and low ones. Moreover, an improved adaptive median filter is used to process regions of high ratio based on the median of sub-windows, while that of low-ratio regions is processed by the adaptive median filter based on the median of current windows. Radar-measured data from Bohai Bay, China are used for algorithm validation and experimental results show that the proposed filtering algorithm performs better than the adaptive median filtering algorithm.

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A Method for Detecting Rainfall From X-Band Marine Radar Images

Cited by 12 publications

References 21 publications

A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar

A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar

A Method for Rainfall Detection and Rainfall Intensity Level Retrieval from X-Band Marine Radar Images

An Improved Adaptive Median Filtering Algorithm for Radar Image Co-Channel Interference Suppression

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