Noise In Radar Images Research Articles

An inland waterway traffic complexity evaluation method using radar sequential images

This paper proposes an inland waterway traffic complexity evaluation method using radar sequential images, which can be used to assist traffic situation awareness by maritime supervisors and reduce their workload. The proposed method consists of four steps: ship detection, ship tracking, ship interaction determination, and traffic complexity evaluation. First, target ships are detected through background subtraction to suppress the interference of radar noise in radar images. Second, the Kalman filter and Hungarian algorithms are introduced to match ship positions in two consecutive sampling intervals. Third, the interaction between ship pairs is determined by integrating the relative distance, encounter angle, and encounter trend. Fourth, ship pairs are marked when the interaction exceeds a set threshold, and a traffic graph is constructed to evaluate traffic complexity using the Edge Density and Strength indicators. The proposed method is tested on selected inland waters of the Yangtze River. The experimental results indicate that the proposed approach can evaluate traffic complexity through radar sequential images in real-time. The proposed approach is interactive and interpretable for maritime supervisors, and it helps them to quickly focus on particular waterways with potential risks.

Research on Radar Target Detection Based on the Electromagnetic Scattering Imaging Algorithm and the YOLO Network

In this paper, the radar imaging technology based on the time-domain (TD) electromagnetic scattering algorithm is used to generate image datasets quickly and apply them to target detection research. Considering that radar images are different from optical images, this paper proposes an improved strategy for the traditional You Only Look Once (YOLO)v3 network to improve target detection accuracy on radar images. The speckle noise in radar images can cover the real information of a target image and increase the difficulty of target detection. The attention mechanisms are added to the traditional YOLOv3 network to strengthen the weight of the target region. By comparing the target detection accuracy under different attention mechanisms, an attention module with higher detection accuracy is obtained. The validity of the proposed detection network is verified on a simulation dataset, a measured real dataset, and a mixed dataset. This paper is about an interdisciplinary study of computational electromagnetics, remote sensing, and artificial intelligence. Experiments verify that the proposed composite network has better detection performance.

Severe Precipitation Recognition Using Attention-UNet of Multichannel Doppler Radar

Quantitative precipitation estimation (QPE) plays an important role in meteorology and hydrology. Currently, multichannel Doppler radar image is used for QPE based on some traditional methods like the Z − R relationship, which struggles to capture the complicated non-linear spatial relationship. Encouraged by the great success of using Deep Learning (DL) segmentation networks in medical science and remoting sensing, a UNet-based network named Reweighted Regression Encoder–Decoder Net (RRED-Net) is proposed for QPE in this paper, which can learn more complex non-linear information from the training data. Firstly, wavelet transform (WT) is introduced to alleviate the noise in radar images. Secondly, a wider receptive field is obtained by taking advantage of attention mechanisms. Moreover, a new Regression Focal Loss is proposed to handle the imbalance problem caused by the extreme long-tailed distribution in precipitation. Finally, an efficient feature selection strategy is designed to avoid exhaustion experiments. Extensive experiments on 465 real processes data demonstrate that the superiority of our proposed RRED-Net not only in the threat score (TS) in the severe precipitation (from 17.6% to 39.6%, ≥20 mm/h) but also the root mean square error (RMSE) comparing to the traditional Z-R relationship-based method (from 2.93 mm/h to 2.58 mm/h, ≥20 mm/h), baseline models and other DL segmentation models.

Reduction of Multiplicative Noise in Radar Images

Introduction. A radar image is an image obtained by remote sensing the earth's surface with a radar device. Radar images are characterized by background graininess caused by speckle noise, which should be filtered to improve the quality of radar images. The structure of speckle noise reduction filters often comprise one or more parameters to control the level of noise smoothing. The values of these parameters have to be selected experimentally. In works devoted to speckle noise filtering, the methods used for selecting filter paraments are rarely clarified.Aim. To present a methodology for selecting the parameters of multiplicative speckle noise filters on a radar image that are optimal in terms of the quality of the resulting image.Materials and methods. The article presents a method for determining the optimal parameters of speckle noise reduction filters. This method was applied to the most conventionally used filters. The search for optimal parameters and testing of the filters were carried out using a specially designed image, which contained the objects most frequently found on radar images. The structural similarity index (SSIM) metric was chosen as a metric that assesses the quality of filtration.Results. After determining the optimal (in terms of SSIM) parameters of speckle noise reduction filters, the filters were compared to select the best filters in terms of the quality of radar image processing. In addition, the operation of the filters under study was tested on images containing various types of objects, namely: large objects, small objects and sharp borders. Knowing which filter copes best with smoothing speckle noise in a particular area and what values of the variable parameters this requires, an optimal quality of radar images can be achieved. Filtering not only improves human perception of radar images, but also reduces the influence of speckle noise during their further processing (object detection, segmentation of areas, etc.).Conclusion. The proposed algorithm allowed optimal parameters for several speckle noise filters to be determined. The quality of filtration was assessed using an expert method (visually) by comparing images before and after filtration, differential images and one-dimensional image slices. The Frost filter and the anisotropic diffusion filter with optimal parameters showed the best processing quality according to the SSIM metric.

Unsupervised estimation of speckle noise in radar images

AbstractSpeckle in radar images has the characteristic of a multiplicative noise. In this article, two unsupervised methods are introduced to estimate the speckle noise statistics using the mean and the standard deviation of small image blocks (4 × 4, or 6 × 6 pixels). Since most radar images contain many small homogeneous areas, a scatter plot of the standard deviation versus the mean can reveal the characteristic of the noise. The blocks from inhomogeneous areas have higher values for the standard deviation, and they are scattered above the main cluster. They are considered as outliers, and should be excluded in the statistical estimation. These two methods are designed for obtaining a linear fit in the scatter plot by ignoring outliers. Several synthetic aperture radar(SAR) images are used for illustration.