SAR image target recognition method based on non-negative matrix factorization of sparse constraint

A non-negative matrix decomposition and sparse constraint technology, which is applied in the field of SAR image target recognition by non-negative matrix decomposition, and can solve problems such as SAR image sparsity that is not reflected.

Active Publication Date: 2015-01-07
XIDIAN UNIV
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Problems solved by technology

However, in this method, the sparsity of the SAR image itself is not reflecte...

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  • SAR image target recognition method based on non-negative matrix factorization of sparse constraint
  • SAR image target recognition method based on non-negative matrix factorization of sparse constraint
  • SAR image target recognition method based on non-negative matrix factorization of sparse constraint

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Embodiment Construction

[0032] refer to figure 1 Shown, the specific implementation steps of the present invention are as follows:

[0033] Step 101: input training sample set and test sample set, the input sample is the SAR target picture in the MSTAR database, such as figure 2 As shown, a is the original image of the BMP2-SN_9563 armored vehicle, b is the original image of the BMP2-SN_9566 armored vehicle, c is the original image of the BTR70-SN_C71 armored vehicle, d is the original image of the T72-SN_132 main battle tank, e It is the original image of T72-SN_812 main battle tank. The MSTAR database is provided by the DARPA / AFRL Moving and Stationary Target Acquisition and Recognition program work, figure 2 In the above, the resolution of each picture is 0.3m*0.3m, and the size is 128*128. In the experiment, the training samples we selected are the pictures when the pitch angle of the SAR is 17°, and the selected test samples are the pictures when the pitch angle is 15°. ° when the picture. ...

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Abstract

The invention belongs to the technical field of image processing, and particularly discloses an SAR image target recognition method based on non-negative matrix factorization of sparse constraint. According to the method, more effective features are extracted by improving a non-negative matrix factorization method to improved recognition precision, and the problems that features extracted in the prior art are not typical and recognition precision is not high are solved. According to the method, a training sample image and a test sample image are pre-processed in the same way, logarithmic transformation is carried out, a pre-processed training sample set is factorized through non-negative matrix factorization of sparse constraint to obtain a basis matrix and a coefficient matrix, a test sample set is projected in a sub space of a basis matrix structure, classification is carried out through an SVM after a feature matrix is obtained, and then classification accuracy is obtained ultimately. Compared with the prior art, the extracted features are more effective and recognition precision can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image feature extraction method, that is, a non-negative matrix decomposition method, specifically a SAR image target recognition method based on sparse constraint non-negative matrix decomposition, which can be widely used in military and in civil applications. Background technique [0002] Synthetic Aperture Radar (SAR) is one of the important means of earth observation and military detection due to its all-day, all-weather, and strong penetrating power. As one of the key technologies of SAR image analysis and interpretation, SAR image target recognition has strong commercial and military value, and has increasingly become a research hotspot at home and abroad. [0003] In the research of SAR image target recognition, it mainly includes image feature extraction research and machine learning machine research. The main purpose of image feature extraction research is t...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F18/23213G06F18/2411
Inventor 慕彩红焦李成师萌熊涛刘若辰刘静杨淑媛王爽云智强王孝齐
Owner XIDIAN UNIV
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