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CFAR (constant false alarm rate) detection and depth learning-based SAR (synthetic aperture radar) target detection method

A target detection and deep learning technology, applied in the field of image processing, can solve the problems of strong clutter and poor detection performance, such as synthetic aperture radar, and achieve the effect of good detection performance and convenient detection threshold adjustment.

Active Publication Date: 2016-11-23
XIDIAN UNIV +1
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Problems solved by technology

The present invention overcomes the problem that the SAR image target detection method in the prior art only utilizes the statistical characteristics of the local area of ​​the SAR image, and can only achieve pixel-level detection, and at the same time achieves end-to-end detection, improving the In complex scenes In complex scenes, such as: more strong clutter, poor detection performance Accuracy of target detection and positioning in synthetic aperture radar SAR images

Method used

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with the accompanying drawings.

[0049] Refer to attached figure 1 , the concrete steps of the present invention are as follows.

[0050] Step 1, acquire SAR images.

[0051] 100 SAR images are randomly selected from the MiniSAR dataset.

[0052] The target coordinate information and category labels corresponding to the selected SAR images are selected from the MiniSAR dataset.

[0053] The selected SAR images, target coordinate information and category labels form the training sample set.

[0054] Step 2, expand the training sample set.

[0055] The target area to be recognized in each SAR image in the training sample set is randomly translated 100 times, and the training sample images after each translation are formed into an expanded training sample set.

[0056] The first step is to read each SAR image in the training sample set in matlab, and obtain the two-dimensional coordinate system corr...

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Abstract

The invention discloses a CFAR (constant false alarm rate) detection and depth learning-based SAR (synthetic aperture radar) target detection method. The method includes the following steps that: (1) SAR images are obtained; (2) a training sample set is expanded; (3) the network structure of a Faster-RCNN model is constructed; (4) a trained RPN model is obtained; (5) a trained Fast-RCNN model is obtained; (6) a finely-adjusted RPN network is obtained; (7) a trained Faster-RCNN model is obtained; and (8) target detection is carried out. With the CFAR (constant false alarm rate) detection and depth learning-based SAR (synthetic aperture radar) target detection method of the invention adopted, end-to-end image-level detection is realized, and major problems in existing SAR target detection technologies can be solved. The method has good detection performance under complex conditions.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a synthetic aperture radar SAR (Synthetic Aperture Radar) target based on constant false scene CFAR (Constant False Alarm Rate) detection and deep learning in the technical field of synthetic aperture radar SAR image target detection Detection method. The invention can accurately detect the target of the synthetic aperture radar SAR image, and can be used for target recognition of the subsequent synthetic aperture radar SAR image. Background technique [0002] Synthetic aperture radar (SAR) has the characteristics of all-weather, all-time, high resolution and strong penetrating power, and is widely used in the fields of military reconnaissance and remote sensing. Radar imaging technology has unique advantages in detecting ground targets, especially ground stationary targets. With the continuous maturity of SAR technology and the continuous improvement of imaging r...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/02G06N3/08G06K9/46
CPCG06N3/02G06N3/084G06V20/13G06V10/40G06F18/214
Inventor 陈渤黄孟缘张志斌刘宏伟
Owner XIDIAN UNIV
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