The feature map is often used for describing the characteristics of key points in image registration applications. Due to feature information being sensitive to rotation and noise, the values of calculation methods for various feature maps would change in different rotation or noise conditions. Consequently, it is unfavorable to obtain the stable descriptor for the same feature point in different images. Specifically, speckle noise damages the synthetic aperture radar (SAR) image seriously, which makes SAR image registration more difficult. In this paper, a novel feature map, which is robust to speckle noise, is proposed. A fully size-adaptive sliding window method is embedded into the ratio of exponentially weighted average method for its noise robustness improvement. Then, a series of Monte Carlo experiments compare the rotation and speckle stabilities among various feature map methods comprehensively. The experimental results reflect that the proposed feature map method is stable while speckle noise increases, and it has less mean orientation difference than others. Moreover, the data of experimental results can be used to support many other analyses on feature maps.
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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