Nearest neighbor subspace SAR target identification method based on multiple sparse descriptions

A target recognition and sparse description technology, applied in the field of image processing, can solve problems such as low precision and poor target recognition effect, achieve the effect of improving accuracy, improving description ability and recognition rate, and overcoming poor description of detailed features

Inactive Publication Date: 2014-06-25
XIDIAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art, and provide a nearest neighbor subspace SAR target recognition method based on multi-sparse description, which can better extract the detailed features of the sample and improve the recognition effect, so as to solve the problem of the prior art. The problem of low recognition accuracy and poor recognition effect on targets with local changes

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  • Nearest neighbor subspace SAR target identification method based on multiple sparse descriptions
  • Nearest neighbor subspace SAR target identification method based on multiple sparse descriptions
  • Nearest neighbor subspace SAR target identification method based on multiple sparse descriptions

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] refer to figure 1 , the realization steps of the present invention are as follows.

[0032] Step 1, preprocessing.

[0033] Input training sample images and test sample images.

[0034] Based on the geometric center of the training sample image and the test sample image, the sub-images of 48×48 pixels are cut out respectively to weaken the influence of the large-area background noise in the synthetic aperture radar SAR image on the recognition performance.

[0035] Divide the amplitude value of the pixel in each sub-image by the maximum value of the amplitude of all pixels in the sub-image to obtain the normalized sub-image of the training sample image and the test sample image, so as to reduce the inhomogeneity of the synthetic aperture radar SAR image The impact of scattering on the recognition performance.

[0036] Step 2, construct dictionary matrix.

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Abstract

The invention discloses a nearest neighbor subspace SAR target identification method based on multiple sparse descriptions and aims at mainly solving the problem that the accuracy is low during target identification and the identification effect on locally-changing targets are poor in the prior art. The nearest neighbor subspace SAR target identification method comprises the steps of 1 performing preprocessing to obtain training samples and normalized sub-images of test sample images; 2 establishing dictionary matrixes to obtain multiple dictionary matrixes identical to categories in number; 3 calculating sparse vectors; 4 calculating reconstruction errors; 5 confirming identification results, utilizing a nearest neighbor subspace formula to use a target category corresponding to a reconstruction error minimum value as an identification result. Compared with the prior art, the identification accuracy of the locally-changing targets is improved. The description capacity and the identification rate of test sample detail features are improved.

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) target recognition method based on the nearest neighbor subspace described by multi-sparse description in the application field of image interpretation and analysis and precise recognition. The invention can realize high-precision synthetic aperture radar SAR target recognition. Background technique [0002] Synthetic Aperture Radar SAR target recognition methods can be mainly divided into template recognition method and model recognition method. The template recognition method generates templates of each type of target in different orientations, and then compares the target to be recognized with all templates, and matches the best matching template. The target category is used as the recognition result; the model recognition method is to extract the characteristics of the target, and compare the characteri...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 吴艳刘明张庆君吉新涛李明
Owner XIDIAN UNIV
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