Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in both civil and military fields. However, due to the special imaging mechanism of SAR images, the aircraft annotating process is easily affected by interferences and noises in the background, leading to a high labeling cost. As most object detection networks are trained in a supervised manner, a serious problem of applying them to SAR aircraft detection tasks is the insufficient training data. To address this problem, we propose an unsupervised domain adaption method for the training of SAR aircraft detectors. First, we propose to transfer knowledge from optical aerial images in which aircraft annotations are easier to obtain. By adopting an image-level domain adaption, the target information in optical images can be utilized for the training of SAR aircraft detectors. Then, CycleGAN is adopted to overcome the discrepancy between optical and SAR domains by image-style translation. To evaluate the effectiveness of the proposed method, we build up an optical-to-SAR aircraft detection dataset (O2SADD) based on existing public datasets. Experiments on O2SADD indicate that the proposed method can significantly improve the performance of SAR aircraft detectors without manually annotating aircraft in SAR images.
Synthetic aperture radar (SAR) image registration is significant for mapping, measurement, navigation, and so on. However, it is problematic for the SAR image alignment because the multiplicative speckle noise in the image and the coherent imagery mechanism of the SAR. Therefore, an advanced feature-based image registration method for the SAR image is proposed in this paper. Firstly, the speckle noise is filtered in natural SAR image based on the weighted nuclear norm minimization, which makes the amount of the false feature point reduce. Secondly, with the defined gradient for the SAR image, the improved SIFT method is employed to extract the feature point and generate the descriptor. The experimental results show that, compared to other methods, the proposed method improves both the accuracy of alignment and utilization of feature point significantly.
Interrupted sampling repeater jamming (ISRJ) is a new type of coherent jamming for wideband radar systems. By copying and repeatedly forwarding the radar transmitting signal slice by slice, ISRJ can generate a series of false targets in the range direction, which significantly impairs radar’s ability to detect and track targets of interest. Therefore, an advanced time-frequency (TF) filtering method for ISRJ suppression is proposed in this paper. Firstly, the radar received echo signal is transformed into TF domain through short-time Fourier transform (STFT). Secondly, based on the discontinuity and high intensity of ISRJ, the ISRJ contaminated regions can be mapped precisely in the TF image by means of histogram energy analysis and subsequent TF energy accumulation. Finally, these regions are removed by a constructed adaptive filter and the jamming-free pulse compression (PC) results can be obtained. Simulation results reveal that, compared with other competing filtering methods, the proposed method can effectively suppress ISRJ and show better robustness under different circumstances.
Due to the increase in data quantity, ship detection in Synthetic Aperture Radar (SAR) images has attracted numerous studies. As most ship targets are small and cover a few pixels in SAR images, the commonly used intersection-over-union (IoU) metric which is sensitive to the location deviation of the bounding box is not suitable to measure the distance between two small ship boxes. To solve this problem, this paper proposes a small ships-oriented detection method based on YOLOX. First, as an anchor-free one-stage detector, YOLOX can achieve state-of-the-art performance without extra anchor parameters. To make a balance between detection accuracy and speed, YOLOX-tiny is adopted as the baseline network. Then, a modified Gaussian Wasserstein distance is proposed. By modeling the bounding boxes as 2D Gaussian distributions, the Modified Wasserstein Distance (MWD) can be used to measure the similarity between the boxes in network training and post-processing. Finally, the proposed method is verified on Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0), and the experimental results show that the proposed MWD can effectively improve the detection performance on small ships.
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