Marine radar rainfall analysis method based on improved AlexNet

A marine radar and analysis method technology, applied in the field of image recognition, can solve the problems of high cost, complicated equipment, and restrictions on the application of weather radar, and achieve the effect of reducing costs and improving recognition rate

Pending Publication Date: 2021-05-14
DALIAN MARITIME UNIVERSITY
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  • Claims
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Problems solved by technology

In terms of rainfall, weather radar is widely used. Although it has advantages in acc

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  • Marine radar rainfall analysis method based on improved AlexNet
  • Marine radar rainfall analysis method based on improved AlexNet
  • Marine radar rainfall analysis method based on improved AlexNet

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Experimental program
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Embodiment 1

[0056] First, the rainfall image is collected to obtain the following image, and then background difference and attenuation compensation processing are performed on the rainfall image, which are the image after background difference and the image after attenuation compensation. After the image is processed, the image corresponding to the identified rain area is intercepted to form a data set , the images of different rain conditions in the dataset are as follows, where (a) is a light rain image, (b) is a moderate rain image, and (c) is a heavy rain image.

[0057] The trained neural network model is used to identify the rainfall image, and the image rain recognition result is obtained. And compared with the original neural network, observe the degree of improvement in the recognition accuracy of the improved neural network. The specific recognition rate is as follows:

[0058] light rain moderate rain heavy rain LeNet-5 86％ 67.25％ 78.38％ Design net...

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Abstract

The invention provides a marine radar rainfall analysis method based on an improved AlexNet neural network. The method comprises the following steps: obtaining a radar original data file; carrying out azimuth extraction on the obtained radar original data file; carrying out merging interpolation processing; carrying out coordinate conversion processing; calling a display function to obtain an echo image; processing a radar rainfall image; intercepting a rainfall area image in combination with data of a meteorological website to obtain a rainfall area image A for neural network training; constructing an AlexNet network model N1; obtaining a feature map A1; constructing a model N2 required by feature fusion; obtaining a feature map A2; and carrying out feature map splicing on the feature map A1 and the feature map A2, carrying out feature fusion, obtaining a feature map A3 and the like, and finally obtaining the recognition accuracy of rain conditions. According to the method, aiming at requirements of rainfall analysis, rainfall images are classified, and the images are trained and recognized by using the improved AlexNet neural network, so that the problem of marine radar rainfall image recognition is solved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method for analyzing rainfall of marine radar based on improved AlexNet. Background technique [0002] Rainfall is one of the most important meteorological elements, and accurate rainfall analysis is of great significance to disaster prevention. With the development of technology, the tradition of weather radar as a rain gauge will be replaced by observation, and great progress has been made to gradually understand more complex weather. In terms of rainfall, weather radar is widely used. Although it has advantages in accuracy, high cost and complicated equipment limit the application of weather radar. Marine radar has certain advantages in cost and installation, and has good sensitivity to rainfall echoes. Contents of the invention [0003] According to the above-mentioned technical problems of high cost and complicated equipment, a method of rainfall analysis for...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/34G06K9/40G06N3/04G06N3/08G01S13/95G01S13/89G01S7/41
CPCG06N3/08G01S13/956G01S13/89G01S7/411G01S7/417G06V10/267G06V10/30G06N3/048G06N3/045G06F18/24G06F18/253G06F18/214Y02A90/10
Inventor 陈晓楠赵春晖
Owner DALIAN MARITIME UNIVERSITY
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