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 AP-CNN and CNN can reach 6.6% in the case of 800 training samples. This means that even if the number of samples is limited, the AP-CNN can obtain optimal classification result.

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.

Various reasons can induce phase error during synthetic aperture radar (SAR) data collection. If phase error is ignored during SAR imaging, the defocused image will be obtained. Although conventional autofocusing methods can eliminate phase error in the full-sampled case, they are not suitable for down-sampled data. Sparse SAR imaging is a technique that combines sparse signal processing with SAR imaging. It can deal with both full- and down-sampled data if the underlying scene admits a sparse representation in a particular domain. Autofocusing methods based on sparse SAR have been developed in the last decades. However, they are known to be developed for spotlight mode. Furthermore, there is a phase ambiguity problem in existing methods, which will affect the uniqueness of the solution if not handled properly. In this paper, we propose a sparse SAR autofocusing imaging method suitable for stripmap mode and give a solution to phase ambiguity problem. The method jointly estimate phase error and reconstruct sparse SAR image in an iterative way. Each iteration consists of sparse imaging based on the phase error corrected echo and an update of the phase error estimation. Experimental results based on simulated and real data verify the effectiveness of the proposed method in coping with the full- or down-sampled data corrupted by the phase error.

In the companion paper (Zhang et al., 2020), we identified two types of calibration models (CMs) that have been widely used in polarimetric calibration algorithms with distributed targets. An optimal method based on the covariance matching estimation technique (COMET), which we refer to as the O method, was used therein but without proof of its optimality. This article supplies the details on parameter estimation using the O method and proves that it is optimal in the mean-squared sense: No other estimate has a smaller mean-squared error. For data affected by the Faraday rotation (FR), the feasibility of the O method is analyzed. Numerical experiments are presented to compare the O method with existing methods and prove its optimality. The differences between the O method and another COMET-based calibration method are discussed. Moreover, we prove that estimating the distortion parameters while preserving the orientation angle is impossible, contrary to what is found by Ainsworth et al. (2006).

Unlike monostatic synthetic aperture radar (SAR), the instantaneous slant range of one-stationary bistatic SAR (OS-BiSAR) is determined by the position of the receiver, transmitter, and target. Therefore, the echo data of OS-BiSAR has strong two-dimensional (2-D) spatial variability, which increases the difficulty of high-precision OS-BiSAR imaging. At present, the frequency-domain algorithm and its improved methods are no longer applicable, and it is impossible to perform high-precision imaging of OS-BiSAR. To solve above problems, in this letter, we propose a novel backprojection (BP) based OS-BiSAR sparse imaging method. In the proposed method, an echo simulation operator is firstly constructed based on BP algorithm to achieve azimuth-range decoupling of the collected echo. Then the considered scene is recovered by solving an L ₁ -norm regularization problem. Experimental results shows that compared with typical matched filtering (MF) based OS-BiSAR imaging algorithm, the proposed method can obtain the SAR image with higher quality especially when the used data is down-sampled, and improves the image target-to-background ratio (TBR) at least 5 dB.

Using the continuous-wave technology to replace the conventional pulse-mode, frequency-modulation continuous-wave (FMCW) synthetic aperture radar (SAR) has shown good potentials of reducing the weight of the system and the sensors' peak transmission power. In order to relax the requirements of data bandwidth and storage, and increase the swath, the SAR system will collect the downsampled data, which makes the traditional matched filtering (MF)-based method unable to recover the considered scene, leading to failed reconstruction. To solve this problem, this letter presents an FMCW SAR sparse imaging method based on the frequency-scaling algorithm (FSA). Experimental results on the real data show that compared with the MF-based FMCW SAR imaging algorithms, the proposed method can improve the recovered image performance effectively. For the sparse surveillance region, it can achieve accurate recovery even from the downsampled data. Because the computational complexity of the proposed method is in the same order as that of MF, the sparse imaging of large-scale scenes can also be realized in the FMCW SAR.