Image-based cloud cover, cloud shape and weather phenomenon inversion device and method

Abstract

The invention discloses an image-based cloud cover, cloud shape and weather phenomenon inversion device and method, and the method comprises the steps: obtaining a sky cloud picture based on ground equipment, and making a data set; then labeling data, manually labeling numerical values of 1-100 in the sky cloud picture, and dividing a data set into a training set, a verification set and a test set according to a proportion of 7: 1.5: 1.5; then, carrying out random scaling, random rotation, random overturning, random cutting and random gamma correction preprocessing operation on the training set, namely, the image; s3, extracting an R channel and a B channel in the RGB image in the step S3, constructing a new channel, namely an R / B channel, based on the R channel and the B channel, and splicing the R / B channel to the original RGB image; according to the method, the features can be more fully utilized, and the total calculation amount is reduced; the three branch tasks can supervise one another, so that the output result is more self-consistent, and the phenomenon that the cloud cover is 100 in a sunny day is reduced, thereby improving the network precision.

Description

technical field [0001] The invention relates to the technical field of weather forecasting, in particular to an image-based cloud amount, cloud shape and weather phenomenon inversion device and method. Background technique [0002] The existing technology mainly calculates the cloud amount by binarizing the weather monitoring image and counting the values ​​of the R channel and the B channel; manually extracting some image features to infer the cloud type; establishing a separate neural network to infer the weather phenomenon. ; The traditional method is not accurate, relies too much on color features, cannot make good use of cloud texture features and other information, and is easily disturbed by other bright objects (sun, white objects, etc.), and cannot identify dark clouds and morning clouds well Sunset, etc.; and using a neural network alone for classification tasks cannot make full use of the intermediate information of different tasks. [0003] In order to improve th...

AI Technical Summary

Problems solved by technology

[0002] The existing technology is mainly to calculate the cloud amount by binarizing the weather monitoring image and counting the values ​​of the R channel and the B channel; manually extracting some image features to infer the cloud type; and separately establishing a neural network to infer the weather phenomenon The accuracy of the traditional method is not high, it relies too much on color features, it cannot make good use of information such as cloud texture features, and it is easily disturbed by other brighter objects (the sun, white objects, etc.), and cannot identify dark clouds and morning glows well. Wanxia, ​​etc.; and using a neural network alone to do classification tasks cannot make full use of the intermediate information of different tasks

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