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SAR image target identification method based on depth increment support vector machine

A support vector machine and support vector technology, applied in the field of image processing, can solve problems such as long training time, low recognition rate, and inability to represent original images well, achieving good real-time performance, reducing training time, and improving target recognition. rate effect

Active Publication Date: 2016-08-31
XIDIAN UNIV
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Problems solved by technology

However, the disadvantage of this method is that when the number of training samples changes, all training samples must be recombined, and the computational complexity increases significantly, resulting in long training time, low recognition efficiency, and poor real-time performance.
The disadvantage of this method is that due to the shallow learning method, the learned features cannot represent the original image well, resulting in a low recognition rate.

Method used

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  • SAR image target identification method based on depth increment support vector machine
  • SAR image target identification method based on depth increment support vector machine
  • SAR image target identification method based on depth increment support vector machine

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] refer to figure 1 , the concrete steps that the present invention realizes are as follows:

[0044] Step 1, input SAR image.

[0045] Input the training sample set and test sample set of known class labels selected in the MSTAR dataset.

[0046] Step 2, image preprocessing.

[0047] The two-dimensional discrete wavelet transform is performed on the training sample set and the test sample set respectively, and the low-frequency images of the training sample set and the test sample set are obtained.

[0048] PCA dimensionality reduction method is used to reduce the dimensionality of the low-frequency images of the training sample set and the test sample set, respectively, and obtain the training sample set and test sample set after dimensionality reduction.

[0049] PCA whitening method is used to whiten the training sample set and test sample set after dimens...

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Abstract

The invention discloses a SAR image target identification method based on a depth increment support vector machine. The method comprises steps of (1), inputting a SAR image; (2) pre-processing the image; (3) constructing an initial training set and an increment training set; (4), initializing the depth increment support vector machine; (5), calculating an initial identification rate of a test sample; (6), updating the depth increment support vector machine; and (7), calculating an identification rate of the test sample. According to the method, advantages of increment learning and depth learning are combined, the depth increment support vector machine is employed to carry out target identification of the SAR image, an increment training sample can be processed, and the depth target information with better discrimination property can be acquired. The method is advantaged in that SAR image target identification precision is improved, and the training time is shortened.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a synthetic aperture radar (Synthetic Aperture Radar, SAR) image target recognition based on a depth incremental support vector machine (Support Vector Machine, SVM) in the technical field of synthetic aperture radar image target recognition method. Aiming at SAR images, the present invention adopts depth increment SVM to carry out target recognition on SAR images, and can be used for target recognition on synthetic aperture radar SAR images. Background technique [0002] Synthetic Aperture Radar (SAR) has the characteristics of all-weather, long-distance, extremely strong penetrating power and high resolution, and is widely used in the national economy and military fields. In the face of the ever-increasing SAR image data collection capabilities, how to accurately and quickly understand and identify these images has attracted more and more attention and attention...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 焦李成屈嵘任婕张丹马文萍马晶晶尚荣华赵进赵佳琦侯彪杨淑媛
Owner XIDIAN UNIV
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