This paper proposes a robust synthetic aperture radar (SAR) automatic target recognition method based on the 3-D scattering center model. The 3-D scattering center model is established offline from the CAD model of the target using a forward method, which can efficiently predict the 2-D scattering centers as well as the scattering filed of the target at arbitrary poses. For the SAR images to be classified, the 2-D scattering centers are extracted based on the attributed scattering center model and matched with the predicted scattering center set using a neighbor matching algorithm. The selected model scattering centers are used to reconstruct an SAR image based on the 3-D scattering center model, which is compared with the test image to reach a robust similarity. The designed similarity measure comprehensively considers the image correlation between the test image and the model reconstructed image and the model redundancy as for describing the test image. As for target recognition, the model with the highest similarity is determined to the target type of the test SAR image when it is denied to be an outlier. Experiments are conducted on both the data simulated by an electromagnetic code and the data measured in the moving and stationary target acquisition recognition program under standard operating condition and various extended operating conditions to validate the effectiveness and robustness of the proposed method.

The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition. This letter gives a solution to the two factors by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs). The ASCs of original SAR images are extracted to reconstruct the target’s image, which not only reduces the noise and background clutters but also keeps the electromagnetic characteristics of the target. Template database are reconstructed at multilevels to simulate various extents of ASC absence in the extended operation conditions. Therefore, the quality of SAR images as well as the completeness of the template database is augmented. Features are extracted from the augmented SAR images, and the classifier is trained by the augmented database for target recognition. Experimental results on the moving and stationary target acquisition and recognition data set demonstrate the validity of the proposed method.

Automatic target recognition (ATR) of synthetic aperture radar (SAR) images is performed on either global or local features. The global features can be extracted and classified with high efficiency. However, they lack the reasoning capability thus can hardly work well under the extended operation conditions (EOCs). The local features are often more difficult to extract and classify but they provide reasoning capability for EOC target recognition. To combine the efficiency and robustness in an ATR system, a hierarchical fusion of the global and local features is proposed for SAR ATR in this paper. As the global features, the random projection features can be efficiently extracted and effectively classified by sparse representation-based classification (SRC). The physically relevant local descriptors, i.e., attributed scattering centers (ASCs), are employed for local reasoning to handle various EOCs like noise corruption, resolution variance, and partial occlusion. A one-to-one correspondence between the test and template ASC sets is built by the Hungarian algorithm. Then, the local reasoning is performed by evaluating individual matched pairs as well as the false alarms and missing alarms. For the test image to be recognized, it is first classified by the global classifier, i.e., SRC. Once a reliable decision is made, the whole recognition process terminates. When the decision is not reliable enough, it is passed to the local classifier, where a further classification by ASC matching is carried out. Therefore, by the hierarchical fusion strategy, the efficiency of global features and the robustness of local descriptors to various EOCs can be maintained jointly in the ATR system. Extensive experiments on the moving and stationary target acquisition and recognition data set demonstrate that the proposed method achieves superior effectiveness and robustness under both SOC and typical EOCs, i.e., noise corruption, resolution variance, and partial occlusion, compared with some other SAR ATR methods.

The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method.

Azimuth sensitivity is a significant characteristic of synthetic aperture radar (SAR) images. Most of the previous SAR target recognition algorithms try to cope with the property by pose estimation or training classifiers which are not sensitive to azimuth. Actually, the azimuth sensitivity can provide discriminative information for target recognition as a supplement to the original spatial image (SI). This letter describes the azimuth sensitivity by the azimuth sensitivity image (ASI) which is constructed by comparing the sub-aperture images of the SI. Then the SI and ASI are classified by the sparse representation-based classification (SRC), respectively. Afterwards, a score-level fusion is employed to combine the two results for robust target recognition. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and the performance is compared with several state-of-the-art methods. The experimental results show that the ASI can complement the SI for effective and robust target recognition.

Multi‐feature decision fusion is a robust way to enhance the performance of synthetic aperture radar (SAR) automatic target recognition (ATR). This study fuses two physically relevant features namely attributed scattering centres and target contour for SAR ATR. The two features provide an intuitive and comprehensive description of targets which can not only promote the ATR performance but also benefit human interpretation. The two features complement each other with weak spatial correlation. Furthermore, both the features can be concisely represented by simple point pattern thus remarkably saving the memory of the feature database. Specific classifiers are designed for the two features whose outputs are fused by the Dempster–Shafer evidence theory to produce the final decision. Experiments based on the moving and stationary target acquisition and recognition database demonstrate the validity of the proposed method.