Solar film image timestamp information extraction method based on deep learning

A deep learning and information extraction technology, applied in the field of solar observation image processing, can solve the time-consuming and labor-intensive problems of manual identification and extraction, and achieve the effect of rapid positioning and identification

A deep learning and information extraction technology, applied in the field of solar observation image processing, can solve the time-consuming and labor-intensive problems of manual identification and extraction, and achieve the effect of rapid positioning and identification

CN110533030AActive Publication Date: 2019-12-03CHINA THREE GORGES UNIV
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  • Solar film image timestamp information extraction method based on deep learning
  • Solar film image timestamp information extraction method based on deep learning
  • Solar film image timestamp information extraction method based on deep learning

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Experimental program
Comparison scheme
Effect test

Embodiment approach

[0067] A general convolutional neural network architecture, including input layer, convolutional layer, pooling layer, fully connected layer and output layer, etc., its structure is as follows figure 1 shown. Through softmax logistic regression, the feature vector output by the output layer is used to classify the input data of the input layer. When the input layer is character image data, the character image can be classified through the classification result of the output layer, and then character recognition can be realized. A convolutional neural network can have multiple convolutional layers, pooling layers, and fully connected layers as needed. figure 1 It is only indicated in its general form.

[0068] The time stamp information in the scanned solar chromosphere film image is extracted by convolutional neural network (CNN), which is mainly divided into three parts, such as figure 2 Shown:

[0069] Step 1, locate and crop the time stamp information area of ​​the imag...

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Abstract

The invention discloses a solar film image timestamp information extraction method based on deep learning, and the method comprises the steps: step 1, positioning and cutting a timestamp information rarea in a solar color ball film image; wherein the timestamp information is year, month, day, hour and minute information used for representing shooting time in the sun color ball film image; step 2,segmenting single characters: further segmenting the characters in the timestamp information area to obtain single characters; and step 3, character recognition: firstly, training a network by adopting a large number of samples, then recognizing the single character obtained by segmentation in the step 2 by using the trained network, and integrating and storing recognition results of the single character. A digital timestamp in a solar observation image film is automatically recognized by a machine, and recognized time information is output. Therefore, the workload of manual identification and time information writing is reduced, the digitization process of the batch of film data can be accelerated, and the precious historical data can be more conveniently used for solar physics research.

Description

technical field [0001] The invention relates to the technical field of solar observation image processing, in particular to a method for extracting timestamp information of solar film images based on deep learning. Background technique [0002] The sun's chromosphere is a layer of atmosphere above the photosphere. As the transition zone from the photosphere to the corona, the magnetic field is not stable, and violent flares often occur. Radiation from solar flares in the chromosphere often appears as elongated ribbons on both sides of the magnetic pole inversion line (PIL), which is considered evidence of a typical pattern of magnetic reconnection. In order to study the phenomenon of flare eruption, relevant personnel need to take pictures and records of the solar chromosphere for a long time. Due to the huge amount of historical data, the time information of a large number of chromospheric images is still presented in the data in the form of images, and has not formed digi...

Claims

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Application Information

Patent Timeline
03 Dec 2019
Publication
CN110533030A
IPC
G06K9/32; G06K9/34; G06K9/62
CPC
G06V20/62; G06V10/267; G06V30/10; G06F18/241
Inventors
曾曙光; 左肖雄