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For the synthetic aperture radar (SAR) automatic target recognition problem, we propose a multiview method based on adaptive decision fusion. Considering the possible independence and correlation among multiview SAR images, a clustering algorithm is first performed to divide them into different view sets. In each view set, the included SAR images are assumed to be highly correlated, which can be effectively classified through joint sparse representation. For the output decisions from different view sets, they are assumed to be relatively independent and contribute disproportionally to the recognition result. Therefore, based on the decisions of individual view sets, adaptive weights are decided based on Shannon entropy theory, which are used to linearly fuse the multiple decisions to determine the target label. The MSTAR dataset is employed in experiments, in which both the standard operating condition and some representative extended operating conditions are set up. The performance of the proposed method is studied in comparison with representative state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Fourier ptychographic microscopy (FPM) is a new, developing computational imaging technology. It can realize the quantitative phase imaging of a wide field of view and high-resolution (HR) simultaneously by means of multi-angle illumination via a light emitting diode (LED) array, combined with a phase recovery algorithm and the synthetic aperture principle. However, in the FPM reconstruction process, LED position misalignment affects the quality of the reconstructed image, and the reconstruction efficiency of the existing LED position correction algorithms needs to be improved. This study aims to improve the FPM correction method based on simulated annealing (SA) and proposes a position misalignment correction method (AA-C algorithm) using an improved phase recovery strategy. The spectrum function update strategy was optimized by adding an adaptive control factor, and the reconstruction efficiency of the algorithm was improved. The experimental results show that the proposed method is effective and robust for position misalignment correction of LED arrays in FPM, and the convergence speed can be improved by 21.2% and 54.9% compared with SC-FPM and PC-FPM, respectively. These results can reduce the requirement of the FPM system for LED array accuracy and improve robustness. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNN) are extensively used in image classification. However, it is typically impossible to receive ideal effects if directly using it in synthetic aperture radar (SAR) image target recognition. During the data acquisition process, it is difficult to acquire a large number of labeled training data and to ensure the image targets in the center. To extract the width and height features of SAR images and reduce the computational complexity, we constructed an asymmetric parallel convolution module. The module avoids severe over-fitting due to limited training samples and effectively deals with displacement changes with test samples. Meanwhile, the residual learning method is used in the algorithm to avoid the deep network degradation and improve algorithm recognition accuracy (RA). Experimental results show that the RA of the DCNN with residual-learning-based APCRLNet reaches 99.75% under standard operating conditions, which is superior to existing recognition methods. Furthermore, the algorithm also performs well for incomplete training samples and test samples with displacement changes. [ABSTRACT FROM AUTHOR]

Vibration greatly impairs the imaging quality of airborne synthetic aperture ladar (SAL), so the vibration phase error must be estimated and compensated before imaging. Limited by the laser's average power, the high pulse repetition frequency of the SAL will result in lower peak-power of the laser, thereby reducing the echo signal's single-pulse signal-to-noise ratio (SNR). In the case of a low SNR, the coherence between channels worsens, and the vibration phase-error compensation method based on interferometry is difficult to apply. We analyze the imaging SNR and resolution of the echo signal when the airborne SAL observes noncooperative targets by intentionally broadening the transmitting and receiving beams, discussing the imaging processing flow of the low SNR signal under conditions of beam-broaden and platform vibration. The data processing flow includes the phase correction of the echo signal using the transmitting reference channel signal. Based on the flight parameters of the airborne SAL, the echo was dechirped and narrow-band filtered to reduce noise. After subaperture division, the spatial correlation algorithm was used to estimate and compensate the vibration phase error, and the range-Doppler algorithm was used to achieve strip imaging. At the same time, the phase gradient autofocus algorithm was introduced to improve the focusing effect. The processing results of flight experiment data of the airborne SAL corresponding to noncooperative targets at 1 km demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Motion error compensation is a crucial aspect of processing inverse synthetic aperture light detection and ranging data. Motion phase error occurs mainly due to the relative motion between the target and the system, as well as vibration of either the system or the target, which significantly affects the image quality of optical synthetic aperture radar. Since spatial targets usually have a non-cooperative motion state with a high degree of motion parameter uncertainty, accurate estimation of cross-range phase error becomes challenging due to the presence of envelope tilt effect. We propose an adaptive compensation method that handles motion errors of maneuvering targets by estimating and compensating various types of errors introduced by the target motion process. Using the geometric and signal models to analyze error components, a compensation model is established that uses envelope contrast and image entropy as fitness functions. The bat algorithm is employed to solve this error model. Simulation and outdoor experimental results demonstrate that the proposed algorithm offers higher accuracy and better stability compared to traditional optimization algorithms. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image despeckling is a preprocessing method. SAR images are, by default, noisy in nature. The kind of noise found in SAR images is called speckle noise. The effect of this noise on SAR images is highly adverse. It degrades the quality of the SAR image, resulting in the loss of vital information. Since SAR images are inherently speckled in nature, it costs a lot of information loss. The removal of such noise from the SAR image is mandatory and is the first step. The elimination of speckle noise from SAR images is called SAR image despeckling. There are various traditional and nontraditional methods of SAR image despeckling based on Bayesian and non-Bayesian techniques. The SAR image despeckling methods based on Bayesian techniques are further subdivided into spatial and transform domains. This paper presents a comparative review of nontraditional perspectives on SAR image despeckling. The comparison is made based on methodology, objectives, merits, and demerits. Its focus is to do analysis of all the latest research done in the field of SAR image despeckling using nontraditional methods. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence and synthetic aperture radar (SAR) imaging technology, methods based on deep learning have been gradually applied to SAR image aircraft detection. However, due to the special SAR imaging mechanism, the imaging results of aircraft targets in SAR images do not have clear features and appear as sparse scattering centers. In addition, airports contain strongly scattering buildings and objects, such as corridor bridges, which will bring huge interference to SAR aircraft target detection. To alleviate these problems, we propose an aircraft scattering information extraction and detection model (SIEDM) based on the spatial imaging characteristics of the targets. First, a scattering center feature extraction module is designed to extract the potential scattering centers of the aircraft targets in the SAR images and acquire the overall structural features of the aircraft targets. In addition, the adaptive noise suppression module is proposed to suppress background interference by learning global information. Finally, we conduct a series of experiments on an SAR image aircraft detection dataset. The experimental results show that the mean average precision of the aircraft SIEDM is 95.7%, which is better than other methods, and the proposed modules in this paper are effective. [ABSTRACT FROM AUTHOR]

Guest Editors Achyut Shankar, Li Zhang, Yu Chen Hu, and Prabhishek Singh introduce the Special Section on Synthetic Aperture Radar Imaging Technology in Deep Learning: New Trends and Viewpoints. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) are utilized for synthetic aperture radar (SAR) image super-resolution reconstruction, affording realistic texture details. However, existing GANs only discriminate the final generated high-resolution (HR) image after two consecutive upsampling processes, which ignore some high-frequency information of the reconstructed images. To resolve this issue, a multiresolution fusion discrimination (MRFD) algorithm is proposed to discriminate the reconstructed feature maps after each upsampling. First, a multiresolution discrimination process discriminates the authenticity of each upsampled feature map separately, which reduces the image distortion imposed during two consecutive upsampling processes. Besides, multiresolution feature fusion further preserves the consistent high-frequency texture structures. Finally, a multiscale dense network extracts image features in different scales, with multiscale dense block's dense connections improving parameter utilization. The experimental results on a SAR dataset demonstrate that the proposed MRFD algorithm performs better in reconstructing the texture details of HR images. [ABSTRACT FROM AUTHOR]

With regard to inverse synthetic aperture radar imaging with limited bandwidth and sparse aperture, it is a challenge to traditional range-Doppler (RD) algorithm. We proposed an innovative two-dimensional (2D) joint sparse imaging algorithm, namely, 2D fast orthogonal matching pursuit (2D-FOMP) algorithm. In the proposed algorithm, one-dimensional OMP (1D-OMP) is extended to 2D-OMP in the complex domain from three aspects of atom recognition, projection update, and residual update. Then, the equivalence between 1D-OMP and 2D-OMP is analyzed theoretically. Meanwhile, two strategies that multi atom recognition and matrix recursive update are added in 2D-OMP to further improve the reconstruction speed of 2D-FOMP. Experimental results based on both simulated and measured data demonstrate that the proposed algorithm has good imaging performance under noisy and sparse conditions. [ABSTRACT FROM AUTHOR]

Time–frequency analysis is a fundamental tool in many applications, such as inverse synthetic aperture radar (ISAR) imaging along the cross-range direction. Traditional time–frequency transformations designed for the general signal suffer low time–frequency resolution or cross-term interference. In this study, a cascaded UNet-like network is applied to the refinement of basic transformations, and another forward regression network is proposed to estimate the signal parameters directly. Both networks can incorporate a priori information and combine different time–frequency transformations efficiently. Several experiments, especially in the ISAR application, are presented to validate the methods. Through this research, the neural network is a promising approach to develop a customized method with high performance for a specific signal processing problem. [ABSTRACT FROM AUTHOR]

A new synthetic aperture radar (SAR) image change detection algorithm was developed using a spatially chaotic model for coherent SAR images. This algorithm is tolerant of misregistration even when the signal-to-noise ratio is low. Despeckling is unnecessary, making the method especially attractive wherever the radiometric changes are subtle. To demonstrate the algorithm’s performance, a varied set of multitemporal polarimetric SAR images was used for testing. As a reference, the simple image difference (DI) technique and the principal-component analysis (PCA) were used for comparison. The proposed method performs very well and can detect minute changes despite the presence of speckle, for which both DI and PCA fail. [ABSTRACT FROM AUTHOR]