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A Radar Image Rainfall Recognition Method

A radar image and recognition method technology, applied in radar image rainfall recognition, using deep learning technology in the field of radar image rainfall recognition, can solve problems such as increasing wave inversion error, changing sea surface roughness, etc., to improve accuracy, feature Obvious, recognition-enhancing effect

Active Publication Date: 2022-06-21
HARBIN ENG UNIV
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Problems solved by technology

Rainfall will also change the roughness of the sea surface and increase the error in wave inversion

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  • A Radar Image Rainfall Recognition Method
  • A Radar Image Rainfall Recognition Method
  • A Radar Image Rainfall Recognition Method

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Embodiment Construction

[0040] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0041] Aiming at the problems existing in the existing marine radar image rainfall detection technology, such as the low recognition accuracy and the influence of sea conditions on the rainfall identification, the present invention proposes a deep convolutional neural network by analyzing the marine radar image and combining with the deep learning theory. A method for learning models to identify rainfall disturbances in radar images. First, the radar original images under different rainfall intensities are suppressed with the same frequency, and the Cartesian box images of the wave monitoring area in the images are selected as the dataset samples, and the improved LeNet-5 model is iteratively trained with the dataset samples; then, The radar image to be detected is subjected to co-frequency interference processing and the Cartesian frame image...

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Abstract

The invention discloses a radar image rainfall recognition method. Firstly, co-frequency interference suppression is performed on radar original images under different rainfall intensities, and the Cartesian frame image of the wave monitoring area in the image is selected as a data set sample, and the data set sample is used to The improved LeNet-5 model is iteratively trained; then, the radar image to be detected is processed by co-channel interference and the Cartesian frame image of the wave monitoring area of ​​the image is extracted, and input into the trained model to obtain the output result probability; finally, By comparing the probability of the output result of the model with the detection threshold, it is determined whether the image is a rainfall image. The invention makes the identification of rainfall images and non-rainfall images easier and more accurate.

Description

technical field [0001] The invention relates to a radar image rainfall identification method, in particular to a radar image rainfall identification method using deep learning technology, and belongs to the technical field of marine remote sensing. Background technique [0002] my country's ocean area accounts for a large proportion, about one-third of the land area. The ocean is rich in a variety of resources and energy, such as organisms, minerals, oil and gas, tourism, etc., and has great potential for development. In recent years, shipborne marine radar has become a mainstream wave observation method. This method has a wide range of measurement and high measurement accuracy, and can be observed around the clock. It can automatically record and display data and adapt to a variety of working environments. The X-band marine radar used in the present invention can measure parameters such as wavelength, wave height, wave direction, wave period and the like of ocean waves. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/95G06N3/04G06N3/08
CPCG01S13/95G06N3/084G06N3/047G06N3/045Y02A90/10
Inventor 卢志忠孙雷吕博群张玉莹郭树渊文保天
Owner HARBIN ENG UNIV
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