Sparse coding of 2D-slice Zernike moments for SAR ATR
| Autor: | Liu Zhouyong, Dong Li, Shujun Liu, Peikang Huang, Xinzheng Zhang, Yunjian Jia |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Synthetic aperture radar
Zernike polynomials Computer science business.industry Feature vector Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Sparse approximation Target acquisition symbols.namesake Automatic target recognition Velocity Moments 0202 electrical engineering electronic engineering information engineering symbols General Earth and Planetary Sciences 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | International Journal of Remote Sensing. 38:412-431 |
| ISSN: | 1366-5901 0143-1161 |
| Popis: | In this article, a new type of feature, named two-dimensional 2D-slice Zernike moments, is proposed for synthetic aperture radar SAR automatic target recognition ATR. Target features play an extremely important role in the ATR system. Pixels with different scattering intensities distribute in different positions in SAR images, which represent target inherent signatures determined by the target’s characteristics, including global structure and local details. To extract these various scattering signatures, we developed a feature extraction technique named 2D-slice Zernike moments 2DS-ZMS, which can capture target global and local scattering field distribution information. First, the multilayer 2D-slices of a SAR image are extracted by uniformly cutting the 3D display SAR image along the amplitude direction. Then Zernike moments of each 2D-slice are calculated. Finally, the 2DS-ZMS of the SAR image are formulated into a column vector, called the feature vector. The obtained feature vectors of the targets are fed into a newly developed classifier, i.e. the sparse representation-based classifier SRC. By projecting the testing sample feature vector on the dictionary made up of training samples feature vectors, the sparse coefficients are solved. The minimum reconstruction residual is adopted as the judgement criterion for predicting the test sample’s class label. Experiments on the moving and stationary target acquisition and recognition MSTAR data set validate the effectiveness and superiority of the proposed method. |
| Databáze: | OpenAIRE |
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