Synthetic aperture radar (SAR) is an earth observation technology that can obtain high-resolution image in allweather and all-time conditions, and hence has been widely used in civil and military applications. SAR target detection and classification are the key processes for the detailed feature information extraction of the interested target. Compared with traditional matched filtering (MF) recovered result, sparse SAR image has lower sidelobes, noise and clutter. Thus it will theoretically has better performance in target detection and classification. In this paper, we propose a novel sparse SAR image based target detection and classification framework. This novel framework first obtains the sparse SAR image dataset by complex approximate message passing (CAMP), which is an L1norm regularization sparse imaging method. Different from other regularization recovery algorithms, CAMP can output not only a sparse solution, but also a non-sparse estimation of considered scene that well preserves the statistical characteristic of the image when protruding the target. Then we detect and classify the targets by using the convolutional neural network (CNN) based technologies from the sparse SAR image datasets constructed by the sparse and non-sparse solutions of CAMP, respectively. For clarify, these two kinds of sparse SAR image datasets are named as DSp and DNsp. Experimental results show that under standard operating conditions (SOC), the proposed framework can obtain 92.60% and 99.29% mAP on Faster RCNN and YOLOv3 by using the DNsp sparse SAR image dataset. Under extended operating conditions (EOC), the mAP value of Faster RCNN and YOLOv3 are 95.69% and 89.91% mAP, respectively. These values based on the DNsp dataset are much higher than the classified result based on the corresponding MF dataset. Index Terms-Sparse synthetic aperture radar (SAR) image, convolutional neural network (CNN), complex approximate message passing (CAMP), target detection and classification. I. INTRODUCTIONA S a kind of high-resolution earth observation technique, synthetic aperture radar (SAR) has all-time and allweather surveillance ability, and has been widely used in many military and civilian fields [1], [2]. Target detection and classification are the key fields of SAR applications,
Due to the imaging mechanism of synthetic aperture radar (SAR), it is difficult and costly to acquire abundant labeled SAR images. Moreover, a typical matched filtering (MF) based image faces the problems of serious noise, sidelobes, and clutters, which will bring down the accuracy of SAR target classification. Different from the MF-based result, a sparse image shows better quality with less noise and higher image signal-to-noise ratio (SNR). Therefore, theoretically using it for target classification will achieve better performance. In this paper, a novel contrastive domain adaptation (CDA) based sparse SAR target classification method is proposed to solve the problem of insufficient samples. In the proposed method, we firstly construct a sparse SAR image dataset by using the complex image based iterative soft thresholding (BiIST) algorithm. Then, the simulated and real SAR datasets are simultaneously sent into an unsupervised domain adaptation framework to reduce the distribution difference and obtain the reconstructed simulated SAR images for subsequent target classification. Finally, the reconstructed simulated images are manually labeled and fed into a shallow convolutional neural network (CNN) for target classification along with a small number of real sparse SAR images. Since the current definition of the number of small samples is still vague and inconsistent, this paper defines few-shot as less than 20 per class. Experimental results based on MSTAR under standard operating conditions (SOC) and extended operating conditions (EOC) show that the reconstructed simulated SAR dataset makes up for the insufficient information from limited real data. Compared with other typical deep learning methods based on limited samples, our method is able to achieve higher accuracy especially under the conditions of few shots.
It is known that a synthetic aperture radar (SAR) image obtained by matched filtering (MF)-based algorithms always suffers from serious noise, sidelobes, and clutters. However, the improvement of the image quality means the complexity of the SAR system will increase, which affects the application of the SAR image. The introduction of the sparse signal processing technique into SAR imaging proposes a new way to solve this problem. Sparse SAR image obtained by sparse recovery algorithms shows a better image performance than the typical complex SAR image with lower sidelobes and higher signal-to-noise ratio. As the most widely applied field of the SAR image, target classification relies on the SAR image with high quality. Therefore, a novel target classification model based on the amplitude and phase information of the sparse SAR image is introduced in this article. First, complex sparse image dataset is constructed by a novel iterative soft thresholding (BiIST) algorithm. Compared with typical regularization-based sparse recovery algorithms, BiIST not only can improve the quality of recovered image, but also can obtain a nonsparse solution with retaining phase information and background statistical distribution of the SAR image. Then, targets are classified by the proposed amplitude-phase convolutional neural network (AP-CNN). Typical SAR target classification networks imitate those on optical image, just using amplitude data. However, considering the particularity of the SAR image, the AP-CNN uses both amplitude and phase for training, which theoretically improves the classification accuracy. Experimental results show that the AP-CNN outperforms the typical amplitude-based CNN in target classification, both under standard operating conditions (SOCs) and extended operating conditions (EOCs). Results under SOC demonstrate that the AP-CNN improves the classification accuracy by 11.46% with only 1000 training samples. Even under EOC, the accuracy gap between the Manuscript
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