Abstract
Sparse representation (SR) has been verified to be an effective tool for pattern recognition. Considering the multiplicative speckle noise in synthetic aperture radar (SAR) images, a product sparse representation (PSR) algorithm is proposed to achieve SAR target configuration recognition. To extract the essential characteristics of SAR images, the product model is utilized to describe SAR images. The advantages of sparse representation and the product model are combined to realize a more accurate sparse representation of the SAR image. Moreover, in order to weaken the influences of the speckle noise on recognition, the speckle noise of SAR images is modeled by the Gamma distribution, and the sparse vector of the SAR image is obtained from q statistical standpoint. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The experimental results validate the effectiveness and robustness of the proposed algorithm, which can achieve higher recognition rates than some of the state-of-the-art algorithms under different circumstances.
Highlights
As one of the most popular application areas of synthetic aperture radar (SAR), SAR target recognition has been deeply exploited due to its great importance in both civil and military areas.SAR target recognition algorithms have been extensively studied in recent years [1,2,3].The algorithms can be generally categorized into template-based ones and model-based ones [4].Template-based algorithms [5,6] are relatively easy to comprehend, but they require a huge storage space
A product sparse representation (PSR) algorithm is proposed for SAR target configuration recognition
The Gamma distribution is employed to model the speckle noise of SAR images and the sparse vector is obtained in the statistical view, which can enhance the robustness of the proposed algorithm under noisy conditions
Summary
As one of the most popular application areas of synthetic aperture radar (SAR), SAR target recognition has been deeply exploited due to its great importance in both civil and military areas.SAR target recognition algorithms have been extensively studied in recent years [1,2,3].The algorithms can be generally categorized into template-based ones and model-based ones [4].Template-based algorithms [5,6] are relatively easy to comprehend, but they require a huge storage space. As one of the most popular application areas of synthetic aperture radar (SAR), SAR target recognition has been deeply exploited due to its great importance in both civil and military areas. SAR target recognition algorithms have been extensively studied in recent years [1,2,3]. The algorithms can be generally categorized into template-based ones and model-based ones [4]. Template-based algorithms [5,6] are relatively easy to comprehend, but they require a huge storage space. The computation burden is heavy, especially when the size of the training sample is large. Owing to the multiple advantages like low storage and computation requirement, robustness to noisy conditions, various model-based algorithms have been proposed for SAR target recognition
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