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Radar image rainfall identification method

A radar image and recognition method technology, which is applied in the field of radar image rainfall recognition and radar image rainfall recognition using deep learning technology, can solve the problems of increasing the wave inversion error and changing the roughness of the sea surface, so as to improve the accuracy rate and the wave The effect of clear texture and improved recognition

Active Publication Date: 2020-09-04
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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  • Radar image rainfall identification method

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Embodiment Construction

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

[0041] Aiming at the problems of low recognition accuracy and the impact of sea conditions on rainfall recognition in the existing navigation radar image rainfall detection technology, the present invention proposes a convolutional neural network depth algorithm based on the analysis of navigation radar images and in combination with deep learning theory. A method for learning models to identify rainfall disturbances in radar imagery. First, the same-frequency interference suppression is performed on the original radar 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 improved LeNet-5 model is iteratively trained using the data set samples; then, Process the radar image to be detected with co-channel interference and extract the Cartes...

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Abstract

The invention discloses a radar image rainfall identification method which comprises the following steps: firstly, carrying out same-frequency interference suppression on radar original images under different rainfall intensities, selecting a Cartesian frame image of a sea wave monitoring area in the images as a data set sample, and carrying out iterative training on an improved LeNet-5 model by utilizing the data set sample; then, performing same-frequency interference processing on a radar image to be detected, extracting a Cartesian frame image of a sea wave monitoring area of the image, and inputting the Cartesian frame image into the trained model to obtain an output result probability; and finally, comparing the model output result probability with a detection threshold to determinewhether the image is a rainfall image. According to the invention, the rainfall image and the non-rainfall image can be identified more simply and conveniently, and the accuracy is higher.

Description

technical field [0001] The invention relates to a radar image rainfall recognition method, in particular to a radar image rainfall recognition method using deep learning technology, which 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 biology, minerals, oil and gas, tourism, etc., with great potential for development. In recent years, shipborne marine radar has become a mainstream way of ocean wave observation. This method has a wide range of measurement and high measurement accuracy. It can observe all-weather, record data and display data fully automatically, and adapt to various working environments. The X-band marine radar used in the present invention can measure parameters such as the wavelength, wave height, wave direction, and wave period of ocean waves. Rain is a natural ...

Claims

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Application Information

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Patent Type & Authority Applications(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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