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Meteorological disaster prediction method based on PPCT

A forecasting method and meteorological technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of high algorithm complexity and poor saliency detection effect, and achieve increased robust performance, simple implementation, and universality. strong effect

Inactive Publication Date: 2021-11-09
JINLING INST OF TECH
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AI Technical Summary

Problems solved by technology

However, most of the current saliency detection algorithms have problems such as high algorithm complexity or poor saliency detection effect, so it is urgent to propose a method that combines deep learning and saliency detection to predict meteorological disasters

Method used

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  • Meteorological disaster prediction method based on PPCT
  • Meteorological disaster prediction method based on PPCT
  • Meteorological disaster prediction method based on PPCT

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Embodiment Construction

[0031] The present invention proposes a meteorological disaster prediction method based on PPCT to effectively predict meteorological disasters, such as figure 1 It is the architecture diagram of the system training model.

[0032] First, use the spaceborne hyperspectral imager to collect hyperspectral image information of atmospheric convective clouds, use the PPCT algorithm to detect the saliency of the hyperspectral image, and obtain the saliency result map of the hyperspectral image;

[0033] PPCT algorithm such as figure 2 As shown, the PPCT algorithm can be expressed as

[0034] P=PCA(X)(1)

[0035]

[0036] Pr=PCA -1 (Q) (3)

[0037] Y=G(Pr) (4)

[0038] Among them, X represents the hyperspectral image, P represents the principal component analysis coefficient of the hyperspectral image, PCA( ) represents the principal component analysis function, Represents the mean value of the principal component analysis coefficient P of the hyperspectral image, round(·) ...

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Abstract

The invention provides a meteorological disaster prediction method based on PPCT, and the method comprises the steps: carrying out the saliency detection of a satellite-borne image through employing a PPCT saliency detection algorithm, obtaining a saliency result of a hyperspectral image, carrying out the recognition prediction of a saliency view through employing VGG, obtaining a meteorological prediction result of each position, and achieving the prediction of the meteorological disaster of each region. In order to solve the problems of low meteorological prediction accuracy, poor real-time performance and the like of the satellite-borne image, the invention provides the saliency detection for the satellite-borne image based on the PPCT (Pulsed Principal Component Transform).

Description

technical field [0001] The invention relates to the field of meteorological disaster prediction, and particularly designs a PPCT-based meteorological disaster prediction method. Background technique [0002] In recent years, with the rapid development of deep learning technology, meteorological disaster prediction technology has been greatly developed. However, hyperspectral image data volume is large and the amount of information is complex, resulting in low accuracy of meteorological disaster prediction technology based on deep learning. , saliency detection is to use transform coding and other technologies to extract the salient area of ​​the image, which can effectively remove the redundant background information of the image and increase the prediction accuracy of the deep learning algorithm. However, most of the current saliency detection algorithms have problems such as high algorithm complexity or poor saliency detection effect, so it is urgent to propose a method th...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2135G06F18/214
Inventor 周洪成
Owner JINLING INST OF TECH