Abstract
Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG)-like feature for SAR ATR (called SAR-HOG). Then, we propose a supervised discriminative dictionary learning (SDDL) method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR), in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC) scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.
Highlights
Automatic target recognition (ATR) is one of the important applications of synthetic aperture radar (SAR) in civilian and military fields
KD (:, j)k2 = 1, ∀ j where Yk = yk × 1T ∈ RK ×nk is the class labels matrix associated with Xk and yk is usually denoted by a one-of-K indicator vector; L(·) measures the classification error between Yk, and the prediction of the classifier with model parameter W ∈ RK × P based on Ak, and L(·) can be a logistic loss function, hinge loss function or square loss function; γ and ρ are regularization parameters; the last term is a regularizer for stability reasons
SVM is implemented by the well-known LIBSVM software [32]; kNN is implemented by the functions in MATLAB Statistics and the Machine Learning Toolbox; sparse representation classifier (SRC) is implemented by the SPAMS software; label consistent K-SVD (LCK-SVD) is implemented by the code supplied in [27]
Summary
Automatic target recognition (ATR) is one of the important applications of synthetic aperture radar (SAR) in civilian and military fields. The process of SAR ATR includes four sequential stages: detection, discrimination, feature extraction and classification. Based on the proposed SDDL and SRC, we propose the SAR ATR classifier, i.e., supervised discriminative dictionary learning and the sparse representation classifier (SDDLSR). The main contributions of this paper can be summarized as follows: (1) We propose a novel local feature for SAR ATR named SAR-HOG, which can effectively capture the main structures of targets in speckled SAR images. (3) We propose a SAR ATR classifier SDDLSR based on SDDL and SRC, in which both the reconstruction error and the classification error are considered.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
