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Low-frequency UWB SAR image target change detection method based on deep learning

A technology of deep learning and change detection, applied in the field of radar, can solve problems such as difficulty in detection and recognition, large dynamic range of images, interference, etc., and achieve the effect of reducing false alarm rate

Pending Publication Date: 2021-12-31
SUN YAT SEN UNIV
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Problems solved by technology

However, compared with optical images, the imaging principle of low-frequency UWB SAR is complex, the image contains a large amount of echo information, the dynamic range of the image is large, and it is interfered by the echo of the tree trunk. Big

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  • Low-frequency UWB SAR image target change detection method based on deep learning
  • Low-frequency UWB SAR image target change detection method based on deep learning
  • Low-frequency UWB SAR image target change detection method based on deep learning

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Embodiment Construction

[0029] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0030] 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;

[0031] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0032] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] like figure 1 As shown, a low-frequency UWB SAR image target change detection method based on deep learning includes the following steps:

[0034] S1: The data set required to build a deep neural network;

[0035] S2: Build a deep neural network;

[0036] S3: Network training and testing.

[0037] This example is a low-frequency UWBSAR image target detection for a data set collected...

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Abstract

The invention provides a low-frequency UWB SAR image target change detection method based on deep learning, and the method employs a deep learning method, and can effectively solve the problems of difficult detection, high false alarm rate and the like in low-frequency UWB SAR image target detection. Target detection is performed on the low-frequency UWB SAR image by using the deep neural network, so that the problem of high identification difficulty in low-frequency UWB SAR image target detection by manually designing features is avoided. The image features are autonomously learned and classified through the convolutional neural network, and the false alarm rate in low-frequency UWB SAR image target detection can be effectively reduced.

Description

technical field [0001] The invention relates to the field of radar technology, and more specifically, to a method for detecting target changes in low-frequency UWB SAR images based on deep learning. Background technique [0002] In modern warfare, there is an urgent need to detect hidden targets covered by foliage, but the detection effect of conventional optical detection equipment and conventional SAR systems is very limited. The rise and development of low-frequency ultra-wideband synthetic aperture radar (UWB SAR) can be compared solve this problem well. Low-frequency UWB SAR is a new system SAR system that combines low-frequency UWB technology and synthetic aperture technology, and generally works in the low-frequency band of microwaves (such as UHF / VHF band). Due to the long wavelength of the signal, the attenuation is very small when detecting hidden targets covered by leaves. Therefore, low-frequency UWB SAR has strong penetration and can effectively detect hidden t...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/10044G06N3/045
Inventor 谢洪途谢晨曦王国倩
Owner SUN YAT SEN UNIV
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