SAR image ship target identification method based on pulse neural network

A spiking neural network and target recognition technology, applied in the field of SAR image ship target recognition based on spiking neural network, can solve the problems of high energy consumption and huge recognition neural network parameters, achieve low energy consumption, improve generalization ability and Robustness, the effect of enhancing image features

Pending Publication Date: 2021-07-13
SUN YAT SEN UNIV
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[0006] The invention provides a SAR image ship target recognition method based on a pulse neural network, which overcomes the tec

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  • SAR image ship target identification method based on pulse neural network
  • SAR image ship target identification method based on pulse neural network
  • SAR image ship target identification method based on pulse neural network

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[0062] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0063] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0064] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0065] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0066] Such as figure 1 As shown, the framework diagram of the SAR image ship target recognition method proposed by the present invention, the whole framework includes performing visual saliency map extraction and pulse encoding on the input image to obtain a pulse sequence, and then through alternately stacking convolutional layers with LIF neurons and full connections The layer and LIF neur...

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Abstract

The invention provides an SAR image ship target identification method based on a pulse neural network, and the method employs a visual saliency map extraction method based on a visual attention mechanism, can enhance the image features, removes the noise influence of speckles, and improves the generalization capability and robustness of a model; then, pulse coding with the step length being T is carried out on the visual saliency map through a Poisson encoder, and a discrete pulse time sequence is obtained so that information can be transmitted by a subsequent network conveniently; then, a convolutional neural network and LIF spiking neurons are utilized to construct a spiking neural network model, better biological characteristics are given to the neural network, and then the information transmission process of the brain can be simulated more accurately; and finally, by using a substitution gradient training method, the problem that the pulse neural network model is difficult to optimize by using gradient descent and back propagation is solved. The method can accurately identify the ship target, and has the advantages of high efficiency and energy conservation.

Description

technical field [0001] The invention relates to the field of image target recognition, and more particularly, relates to a SAR image ship target recognition method based on a pulse neural network. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution imaging radar with imaging characteristics such as all-weather, all-time, and weather-free. Dynamic and real-time observation of land and ocean has become an important part of the observation system of the earth and the sea, and is currently the research focus of remote sensing technology. It is not only widely used in national economic construction, ecological environment protection and other fields, but also plays an increasingly important role in national security and military fields. With the launch of Gaofen-3 and other satellites, more and more high-resolution SAR images are provided, which further promotes the development and application of SAR image interpretation technology. [0003] In rec...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/04G06N3/08G06V20/13G06V10/462G06V2201/07
Inventor 谢洪途李金膛王国倩陈曾平
Owner SUN YAT SEN UNIV
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