Abstract
Dictionary construction is a key factor for the sparse representation- (SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. The proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. The accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.
Highlights
Due to the powerful day and night working ability under inclement weather conditions, the synthetic aperture radar (SAR) has attracted increasing popularity in recent years [1]
Due to the coherent imaging mechanism of SAR, the speckle noise in SAR images is multiplicative [18, 19]. erefore, different from the presented work, in which SAR images are modelled by the additive model [16], we describe SAR images in a more precise way by the product model in this paper. e motivation of the proposed method is to fuse the advantages of SR with learned dictionaries and the product model together to improve the robustness of the recognition under various severe conditions
E original SR algorithm [20], the monogenic signalbased SR algorithm (MSR) [9], the joint SR algorithm (JSR) [11], the sparse representation- (SR-)based algorithm using the K-SVD algorithm [25] to learn dictionaries (DL), and the SR-based algorithm using the statistical dictionary learning algorithm [16] to learn dictionaries (SDL) are chosen to be competitors to test the advantage of the proposed method
Summary
Due to the powerful day and night working ability under inclement weather conditions, the synthetic aperture radar (SAR) has attracted increasing popularity in recent years [1]. As one of the hottest topics related to SAR remote-sensing applications, SAR automatic target recognition (ATR) focuses on the recognition of the interest targets from 2dimensional (2D) high-resolution SAR images. SAR ATR algorithms can be roughly categorized into template-based methods and model-based methods [2, 3]. With respect to template-based methods, model-based ones can achieve better performance. Model-based methods often involve two related parts, which are feature extraction and classifier design [4]. Plenty of effective features have been exploited over the past decades, such as physical models [5], geometrical characteristics [6], and mathematical features [7]. E performance of these algorithms heavily relies on the precision of feature extraction Plenty of effective features have been exploited over the past decades, such as physical models [5], geometrical characteristics [6], and mathematical features [7]. e performance of these algorithms heavily relies on the precision of feature extraction
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