Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has proposed a high-efficiency and low-energy ship recognition strategy based on the spiking neural network (SNN) in the SAR images. First, the visual attention mechanism is used to extract the visual saliency map from the SAR image, and then the Poisson encoder is used to encode it into a spike train, which can suppress the background noise while retaining the visual saliency feature of the SAR image. Besides, an SNN model integrating the time-series information is constructed by combining the leaked and integrated firing spiking neurons with the convolutional neural network (CNN), which can use the firing frequency of the spiking neurons to realize the ship recognition in SAR images. Finally, to solve the problem that SNN model is difficult to train, the arctangent function is used as the surrogate gradient function of the spike emission function during the backpropagation. Hence, applying this backpropagation method to the training process can optimize the SNN model. The experimental results show the following: (1) the proposed strategy can more accurately recognize the ship in the SAR image, and the F1 score can reach 98.55%, which has a better recognition performance than the other traditional deep learning methods; (2) the proposed strategy has the least amount of model parameters (only 3.11MB), which is far less than the model parameters of the other traditional deep learning methods; (3) the proposed strategy has fewer operations (only 17.97M) and can reach 1/30 time of operands of the other traditional deep learning methods, which shows the high efficiency of the proposed strategy using the spike emission signals; (4) the proposed strategy has the energy loss of 1.38 × 10−6J, which can achieve the low energy advantage of nearly three orders of the magnitude compared to the other traditional deep learning methods, indicating that the proposed strategy has a significant energy efficiency.
Due to the limitations of the horizontal bounding boxes for locating the oriented ship targets in synthetic aperture radar (SAR) images, the rotated bounding box (RBB) has received wider attention in recent years. First, the existing RBB encodings suffer from boundary discontinuity problems, which interfere with the convergence of the model, and then lead to some problems, such as the inaccurate location of the ship targets in the boundary state. Thus, from the perspective that the long-edge features of the ships are more representative of their orientation, the long-edge decomposition RBB encoding has been proposed in this paper, which can avoid the boundary discontinuity problem. Second, the problem of the positive and negative samples imbalance is serious for the SAR ship images because only a few ship targets exist in the vast background of these images. Since the ship targets of different sizes are subject to varying degrees of interference caused by this problem, a multiscale elliptical Gaussian sample balancing strategy has been proposed in this paper, which can mitigate the impact of this problem by labeling the loss weights of the negative samples within the target foreground area with multiscale elliptical Gaussian kernels. Finally, experiments based on the CenterNet model were implemented on the benchmark SAR image dataset SSDD (SAR ship detection dataset). The experimental results demonstrate that our proposed long-edge decomposition RBB encoding outperforms other conventional RBB encodings in the task of oriented ship detection in SAR images. In addition, our proposed multiscale elliptical Gaussian sample balancing strategy is effective and can improve the model performance.
P-band ultra-wideband synthetic aperture radar (UWB SAR) not only has the characteristics of the high-resolution imaging, but also has the well capability of the foliage penetrating, which is potential of detecting and imaging the concealed target under the vegetation. However, there are a lot of the radio, television and mobile communication signals in the P-band, which are called as the radio frequency interference (RFI) signals. These RFI signals are mixed with target echo signals, which will cause the serious interference in the P-band UWB SAR imaging. The traditional notch method is easy to implement the RFI suppression, so it has been widely used. However, the traditional notch method is to notch each pulse echo individually, which has a high computational complexity. At the same time, the RFI suppression of each pulse echo separately will always lead to a large amount of the residual interference, so the traditional notch method has the poor RFI suppression effect. Based on the traditional notch method, this paper proposes an RFI suppression method based on the two-dimensional frequency domain (2DFD) notch, which can realize one-time processing of all echo pulses so that improve the efficiency of the RFI suppression. Meanwhile, because the bandwidth of the RFI signal is much smaller than that of the SAR echo signal, converting the received SAR echo signal to the 2DFD can further concentrate the energy of the RFI signals, so it has the better RFI suppression effect. The simulation results show that the proposed RFI suppression method based on the 2DFD notch can not only improve the efficiency of the RFI suppression but also have the better effect of the RFI suppression.
Low frequency ultra-wideband bistatic synthetic aperture radar (UWB BSAR) system is able to penetrate the foliage, get the high-resolution BSAR image, and offer the increased target information. In this paper, the low frequency UWB BSAR electromagnetic scattering characteristic is analyzed. First, the target under the foliage are modeled and discussed. Moreover, the method of moment (MoM) is proposed for the electromagnetic scattering characteristic. Finally, the simulation experiment is conducted for the modeling and analyzing of the electromagnetic scattering characteristic of the targets, which verifies the correctness of the low frequency UWB BSAR electromagnetic scattering characteristic.
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