Target Recognition in SAR Images Based on Information-Decoupled Representation

Abstract

This paper proposes an automatic target recognition (ATR) method for synthetic aperture radar (SAR) images based on information-decoupled representation. A typical SAR image of a ground target can be divided into three parts: target region, shadow and background. From the aspect of SAR target recognition, the target region and shadow contain discriminative information. However, they also include some confusing information because of the similarities of different targets. The background mainly contains redundant information, which has little contribution to the target recognition. Because the target segmentation may impair the discriminative information in the target region, the relatively simpler shadow segmentation is performed to separate the shadow region for information decoupling. Then, the information-decoupled representations are generated, i.e., the target image, shadow and original image. The background is retained in the target image, which represents the coupling of target backscattering and background. The original image and generated target image are classified using the sparse representation-based classification (SRC). Then, their classification results are combined by a score-level fusion for target recognition. The shadow image is not used because of its lower discriminability and possible segmentation errors. To evaluate the performance of the proposed method, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under both standard operating condition (SOC) and various extended operating conditions (EOCs). The proposed method can correctly classify 10 classes of targets with the percentage of correct classification (PCC) of 94.88% under SOC. With the PCCs of 93.15% and 75.03% under configuration variance and 45 • depression angle, respectively, the superiority of the proposed is demonstrated in comparison with other methods. The robustness of the proposed method to both uniform and nonuniform shadow segmentation errors is validated with the PCCs over 93%. Moreover, with the maximum average precision of 0.9580, the proposed method is more effective than the reference methods on outlier rejection.

SAR image classification based on multi‐feature fusion decision convolutional neural network

“…In order to further verify the stability of the MFFD-CNN model, we use four categories of target (2S1, BRDM2, T72(A64), ZSU-234) [34], where the 17 • depression angle is used for training, the data of 30 • and 45 • depression angles are utilized for testing. The experiment results about CNN, FCNN and MFFD-CNN are as shown in Tables 10 and 11.…”

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

“…Papson and Narayanan applied target shadow to SAR ATR and demonstrated its validity [28]. Chang and You constructed the information-decoupled components based on target region and shadow [29]. Te intensity distributions of SAR images were usually described by transformation features by mathematical projection or signal processing techniques.…”