SAR Automatic Target Recognition Based on Attribute Scattering Center Model and Discriminative Dictionary Learning

Synthetic aperture radar (SAR) automatic target recognition plays a crucial role in both national defense and civil applications. Although many methods have been proposed, it remains one of the most challenging tasks to identify different terrain vehicle targets. In this paper, a novel method is pro...

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Veröffentlicht in:IEEE sensors journal Jg. 19; H. 12; S. 4598 - 4611
Hauptverfasser: Li, Tingli, Du, Lan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 15.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
attribute scattering center model (ASCM)
Automatic target recognition
Convolution
Defense programs
Dictionaries
discriminative dictionary learning
Feature extraction
Genetic algorithms
label consistent K-singular value decomposition (LC-KSVD)
Learning
locality constrained linear coding (LLC)
Machine learning
Mathematical models
Parameters
Scattering
Sensors
spatial pyramid matching (SPM)
Support vector machines
Synthetic aperture radar
Synthetic aperture radar (SAR)
Target acquisition
Target recognition
ISSN:1530-437X, 1558-1748
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Zusammenfassung:Synthetic aperture radar (SAR) automatic target recognition plays a crucial role in both national defense and civil applications. Although many methods have been proposed, it remains one of the most challenging tasks to identify different terrain vehicle targets. In this paper, a novel method is proposed based on the attribute scattering center model (ASCM) and discriminative dictionary learning. This method contains three main stages. In the first stage, the low-level local features are generated by the convolution operation between SAR patches and the ASCMs with different parameters. These parameters are selected by genetic algorithm in advance with labeled patches. In the feature coding stage, a discriminative dictionary learning method is proposed with the label consistent and locality constraint on the sparse coefficient to incorporate the label information and local geometric information into the sparse representation, which is called label consistent and locality constraint discriminative dictionary learning (LcLcDDL) method. The low-level local features are sparsely coded using the dictionary learned by LcLcDDL method. In the feature pooling stage, the spatial pyramid matching method with max pooling is utilized to aggregate the coding coefficients of the low-level local features to generate the high-level global feature for each patch. The generated high-level global feature is feed into the linear support vector machine to recognize which classes the patch belongs to. Experimental results conducted on the moving and stationary target acquisition and recognition data set show that the performance of the proposed method is better than several available approaches.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2901050