Grid parameterization-based remote sensing image target recognition training sample generation method

A grid parameterization and remote sensing image technology, applied in the field of computational geometry and deep learning, can solve the problem of high cost, achieve the effect of diversity and richness guarantee, high robustness and practicality

Pending Publication Date: 2022-03-25
CHINA ACADEMY OF SPACE TECHNOLOGY
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  • Application Information

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Problems solved by technology

In this process, the parameter training of the entire network often requires massive data as training samples, and the cost is relatively high.

Method used

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  • Grid parameterization-based remote sensing image target recognition training sample generation method
  • Grid parameterization-based remote sensing image target recognition training sample generation method
  • Grid parameterization-based remote sensing image target recognition training sample generation method

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Embodiment Construction

[0039] Specific embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0040] In order to generate training samples for remote sensing image target recognition, the present invention provides a training sample enhancement method based on grid parameterization and Mobius transformation, which is used to generate additional training data for deep learning network training. The invention can mathematically guarantee the conformality of the transformation between the augmented sample and the recognition target in the original sample, that is, the shape of the object does not change greatly, and can carry out relatively large transformation on the map geographic picture information as the background , so as to improve the richness and diversity of training samples.

[0041] In geometry and complex variable analysis, the Möbius transformation on the complex plane can be written as a rational function In the f...

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Abstract

A remote sensing image target recognition training sample generation method based on grid parameterization comprises the following steps: for a given original image, constructing a grid of a corresponding size according to the size of the given original image and triangularizing the grid to obtain an initial triangular grid M, and parameterizing the initial triangular grid M to obtain a parameterization grid G '; after unitization is carried out on the parameterized grid G ', two fixed points gamma 1 and gamma 2 on the parameterized grid G' are selected, corresponding Mobius transformation is obtained through reverse deduction of gamma 1 and gamma 2, then Mobius transformation is carried out on the parameterized grid G ', and unitization is carried out on the transformation result; and finally, according to a real-time pipeline rendering rule, taking the grid G'obtained after Mobius transformation as a parameterized coordinate, taking the original image as a texture, and pasting the texture back to the initial triangular grid M to obtain an augmented image.

Description

technical field [0001] The invention relates to a method for generating training samples for remote sensing image target recognition based on grid parameterization and Mobius transformation, and belongs to the technical fields of computational geometry and deep learning. Background technique [0002] In remote sensing image target recognition tasks, deep learning methods are usually used to detect targets such as ships, aircraft, and cars. In this process, the parameter training of the entire network often requires massive data as training samples, and the cost is relatively high. [0003] Generally, when the training data is relatively small, data augmentation is a common method to expand it and generate new samples. Its purpose is to increase the richness of the training data set and make the training data as diverse as possible, so that the training The resulting network model has stronger generalization ability. According to the timing of data processing, data augmenta...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/10G06K9/62G06F17/16G06V10/774G06V10/74
CPCG06F17/16G06F18/22G06F18/214
Inventor 任术波曾骏杰陶滢辛宁张磊任军强高梓贺
Owner CHINA ACADEMY OF SPACE TECHNOLOGY
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