SAR image target recognition method based on sparse representation

A sparse representation and automatic target recognition technology, applied in the field of image processing, can solve the problems of low recognition accuracy and increased computational complexity, and achieve the effect of improving the recognition rate

Active Publication Date: 2014-08-13
XIDIAN UNIV
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Problems solved by technology

This method uses a local constraint encoding method, although it can keep the position information of the sample points well, but it still needs to estimate the position of the target, which increases the computational complexity, and its recognition accuracy is not high

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  • SAR image target recognition method based on sparse representation
  • SAR image target recognition method based on sparse representation
  • SAR image target recognition method based on sparse representation

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Embodiment Construction

[0026] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0027] Step 1, extract the scale-invariant feature SIFT of the SAR image in the training sample set and the test sample set.

[0028] Input the training sample set and test sample set in the measured SAR ground stationary target database MSTAR provided by the US DARPA / AFLMSTAR project team, for each sample in the two sample sets, uniformly sample with a step size of 6 pixels, and extract each sample The d-dimensional scale-invariant feature SIFT in the 16×16 sub-block around the point is obtained to obtain the SIFT feature matrix X=[x 1 ,x 2 ,...,x i ,...,x N ]∈R d×N , where R represents the set of real numbers, x i Indicates the i-th SIFT feature, i=1,2,...,N, N indicates the number of features in the sample, and d indicates the SIFT feature dimension d=128.

[0029] Step 2: Randomly extract E=8000 features from the scale-invariant feature SIFT obtained from the trai...

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Abstract

The invention discloses an SAR image target recognition method based on sparse representation. The SAR image target recognition method based on sparse representation mainly resolves the problem that an existing method is complex in preprocessing and difficult in estimation of an azimuth angle. The SAR image target recognition method based on sparse representation comprises the steps of (1) extracting partial features of an image and studying a recognizable dictionary through a diversity density function; (2) carrying out sparse encoding on each partial feature through the dictionary, and then carrying out space pooling on each divided sub-area through a space domain pyramid structure to obtain feature vectors of the sub-areas samples of a training set and a test set; (3) weighing the corresponding sub-areas of a test sample according to the sparsity of each sub-area of the test sample; and (4) combining the weighed sub-areas together and recognizing the image through a sparse representation method. Compared with the prior art, the SAR image target recognition method based on sparse representation has high robustness for shielded and partial noise, improves the recognition accuracy of an SAR target without estimating the azimuth angle, and can be used for image processing.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a target recognition method for SAR images, which can be used for the detection and defense of ground targets. Background technique [0002] The automatic target recognition technology of high-resolution synthetic aperture radar SAR is an important part of SAR image analysis and interpretation, and has important practical value. Because SAR can acquire high-resolution landmark image data in a large area all-weather, all-day, and is not limited by smoke, dust, and fog, it has a wide range of application backgrounds. [0003] So far, SAR image target recognition methods mainly include methods based on image template matching and methods based on feature template matching. Among them, the method based on image template matching needs to estimate the azimuth angle of the image and establish a template that matches the test sample. This method is simple and easy to implement...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/66
Inventor 焦李成王少娜马文萍刘红英杨淑媛王爽熊涛刘静
Owner XIDIAN UNIV
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