Data Augmentation Based on Attributed Scattering Centers to Train Robust CNN for SAR ATR

Abstract

Driven by the good classification performance of the convolutional neural network (CNN), this study proposes a CNN-based synthetic aperture radar (SAR) target recognition method. In this paper, a novel data augmentation algorithm is proposed via target reconstruction based on attributed scattering centers (ASC). The ASCs reflect the electromagnetic phenomenon of SAR targets, which can be used to reconstruct the target's characteristics. The sparse representation (SR) algorithm is first employed to extract the ASCs from a single SAR image. Afterward, some of the extracted ASCs are selected to reconstruct the target's image. By repeating the process, many new images can be generated as available training samples. In the classification stage, a CNN architecture is designed and trained by the augmented samples. For the test sample, it is also reconstructed using all the extracted ASCs thus relieving the interferences caused by the clutters or noises in the background. Finally, the reconstructed image from the test sample is classified based on the trained CNN. The reconstructed image from the ASCs can reduce the clutters and noises thus enhancing the image quality. More importantly, the generated new training samples could cover more operating conditions, which may probably occur in SAR target recognition. Therefore, the trained CNN can work more robustly in different situations. In the experiments, the moving and stationary target acquisition and recognition (MSTAR) dataset is used to evaluate the performance of the proposed approach. This method could classify the 10 classes of targets with an accuracy of 99.48% under the standard operating condition (SOC). For the extended operating conditions like configuration variance, depression angle variance, noise corruption, and partial occlusion, the proposed method also displays superior performance over some baseline algorithms drawn from state-of-the-art literature. INDEX TERMS Convolutional neural network (CNN), synthetic aperture radar (SAR), target recognition, attributed scattering center (ASC), data augmentation.

Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

“…In recent years, deep learning technology, especially the convolutional neural network (CNN) has emerged as a powerful tool for image construction and processing [16][17][18]. Previously, the CNN has been successfully applied to implement speckle elimination [19,20], target classification [21,22], and recognition [23] in the field of SAR imaging. Besides, CNN-based fast computed tomography (CT) image construction has also been proposed to address the sparseview problem [24].…”

Product Dictionary Learning-Based SAR Target Configuration Recognition

“…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.…”

“…As for the classifier design, some advanced classifiers such as support vector machine (SVM) [8], sparse representation [7,[9][10][11], and convolutional neural networks (CNN) [1,5,8] have been employed. Algorithms based on CNN [1,5,8] or other deep learning [3] have been enriched enormously. However, due to the special complexity of SAR images and shortage of data amount, these algorithms usually suffer from overfitting and local minima [2].…”

Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition

“…e scattering center is the typical scattering feature with several applications in SAR ATR [25][26][27][28][29][30][31]. A Bayesian matching scheme of attributed scattering centers was developed in [26] for target recognition.…”

“…Zhang proposed a noise-robust method using attributed scattering centers [29]. Furthermore, the attributed scattering centers were employed to partially reconstruct the target to enrich the available training samples [30,31]. In addition to the use of single-type features, many multifeature SAR ATR methods were designed in the present works [32][33][34][35][36].…”