Deep convolutional neural network for meteorology target detection in airborne weather radar images

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.

Lira-YOLO: a lightweight model for ship detection in radar images

For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules.The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once(Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision(mAP) indicators on the mini-RD and SAR ship detection dataset(SSDD) reach 83.21% and 85.46%, respectively,which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

A novel algorithm for ocean wave direction inversion from X-band radar images based on optical flow method

As one of the important sea state parameters for navigation safety and coastal resource management, the ocean wave direction represents the propagation direction of the wave. A novel algorithm based on an optical flow method is developed for the ocean wave direction inversion of the ocean wave fields imaged by the X-band radar continuously. The proposed algorithm utilizes the echo images received by the X-band wave monitoring radar to estimate the optical flow motion, and then the actual wave propagation direction can be obtained by taking a weighted average of the motion vector for each pixel. Compared with the traditional ocean wave direction inversion method based on frequency-domain, the novel algorithm is fully using a time-domain signal processing method without determination of a current velocity and a modulation transfer function（MTF）. In the meantime,the novel algorithm is simple, efficient and there is no need to do something more complicated here. Compared with traditional ocean wave direction inversion method, the ocean wave direction of derived by using this proposed method matches well with that measured by an in situ buoy nearby and the simulation data. These promising results demonstrate the efficiency and accuracy of the algorithm proposed in the paper.

Investigation of Simulating Radar Images Concerning the Multipath Scattering Effect

In the composed system of a target and rough surface, the electromagnetic scattering mechanism, especially the multipath scattering, is investigated. Using physical optics double bouncing algorithm, the multipath scattering model of the system has been established. Simulated by a wide-band radar signal and based on fractal rough surface, the artificial echo of the target has been obtained in virtue of the established multipath scattering model. By simulating to image the target in one dimension using the artificial echo, two kinds of range profiles are attained. It is found that one is from the target and the other is from the multipath scattering effect. Key words multipath scattering - radar imaging - rough surface scattering CLC number O 451 Foundation item: Supported by the Key Laboratory Foundation of National Defense Science and Technology (99JS93. 1. 2. JW1204)Biography: Yang Chun-hua(1978-), male, Ph. D candidate, research direction: radio wave propagation and wiresless communication.

Texture invariant estimation of equivalent number of looks based on log-cumulants in polarimetric radar imagery

A novel estimation of the equivalent number of looks (ENL) is proposed in statistical modeling of multilook polarimetric synthetic aperture radar (PolSAR) images for the product model, which is based on the log-determinant moments (LDM). The LDM estimators discovered by looking at certain log-cumulants of the intensities of different polarization channels and the multilook polarimetric covariance matrix, which can be used for both the Gaussian model and all product models. This estimator has analytic expressions, and uses the full covariance matrix and intensities as input, which makes more statistical information available. Experiments based on simulated data and real data are performed. The comparisons among the widely used methods of equivalent number of looks (ENL) estimation for the product model such as K and G0 distributions show that the performance of the LDM estimator is outstanding. The performance of estimators for the real data of San Francisco and Flevoland is analyzed and the results are according to those of simulated data. Finally, it can be concluded that the LDM estimator is well robust to each product model with low computational complexity and high accuracy. ? 1990-2011 Beijing Institute of Aerospace Information.

Sea surface reconstruction from marine radar images using deep convolutional neural networks

The sea surface reconstructed from radar images provides valuable information for marine operations and maritime transport.The standard reconstruction method relies on the three-dimensional fast Fourier transform(3D-FFT),which introduces empirical parameters and modulation transfer function(MTF)to correct the modulation effects that may cause errors.In light of the convolutional neural networks’(CNN)success in computer vision tasks,this paper proposes a novel sea surface reconstruction method from marine radar images based on an end-to-end CNN model with the U-Net architecture.Synthetic radar images and sea surface elevation maps were used for training and testing.Compared to the standard reconstruction method,the CNN-based model achieved higher accuracy on the same data set,with an improved correlation coefficient between reconstructed and actual wave fields of up to 0.96-0.97,and a decreased non-dimensional root mean square error(NDRMSE)of around 0.06.The influence of training data on the deep learning model was also studied.Additionally,the impact of the significant wave height and peak period on the CNN model’s accuracy was investigated.It has been demonstrated that the accuracy will fluctuate as the wave steepness increases,but the correlation coefficient remains above 0.90,and the NDRMSE remains less than 0.11.

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.

Three-dimensional positions of scattering centers reconstruction from multiple SAR images based on radargrammetry

A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.

SAR image denoising based on wavelet-fractal analysis

Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum of the signal is different from that of noise. This difference is used to alleviate the noise produced by SAR image.The method to denoise SAR image using the process based on wavelet-fractai analysis is discussed in detail. Essentially, the present method focuses on adjusting the Hoelder exponent α of multifractal spectrum. After simulation, α should be adjusted to 1.72-1.73. The more the value of α exceeds 1.73, the less distinctive the edges of SAR image become. According to the authors denoising is optimal at α=1.72-1.73. In other words, when α =1.72-1.73, a smooth and denoised SAR image is produced.

An Algorithm for Estimating the Object Depth Accurately by Ground-Penetrating Radar

Ground penetrating radar (GPR) is a remote sensing technique used to obtain information on subsurface features from data collected over the surface. We propose an automatic algorithm for estimating object depth using f-k migration and velocity scanning methods in a homogeneous medium. To improve the accuracy of the algorithm, the formula used to calculate the GPR valid lateral aperture is also presented. Experimental results show that the relative estimating error of depth is as low as 5% in a homogeneous medium.

Application of improved equivalent edge currents in synthetic aperture radar imaging

Target modeling and scattering function calculating are important prerequisites and groundwork for the synthetic aperture radar（SAR） imaging simulation.According to the difficult problems that normal methods cannot calculate the scattering function of electrically large object under the condition to wideband,an effective method of improved equivalent edge currents is presented and applied to SAR imaging simulation for the first time.This method improves calculating velocity and has relatively high precision.The concrete steps of applying the method are given.By way of the simulation experiment,the effectiveness of the method is verified.

Recognition of the major scattering sources on complex targets based on the high frequency radar cross section integrated calculation technique

Based on the high frequency （HF） integrated radar cross section （RCS） calculation approach, a technique of detecting major scattering source is developed by using an appropriate arithmetic for scattering distribution and scattering source detection. For the perfect adaptability to targets and the HF of the HF integrated RCS calculation platform, this technique is suitable to solve large complex targets and has lower requirement to the target modeling. A comparison with the result of 2-D radar imaging confirms the accuracy and reliability of this technique in recognition of the major scattering source on complex targets. This technique provides the foundation for rapid integrated evaluation of the scattering performance and 3-D scattering model reconstruction of large complex targets.

Novel imaging methods of stepped frequency radar based on compressed sensing

The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.

Parameter estimation for rigid body after micro-Doppler removal based on L-statistics in the radar analysis

In traditional inverse synthetic aperture radar （ISAR） imaging of moving targets with rotational parts, the micro-Doppler （m-D） effects caused by the rotational parts influence the quality of the radar images. Recently, L. Stankovic proposed an m-D removal method based on L-statistics, which has been proved effective and simple. The algorithm can extract the m-D effects according to different behaviors of signals induced by rotational parts and rigid bodies in time-frequency （T-F） domain. However, by removing m-D effects, some useful short time Fourier transform （STFT） samples of rigid bodies are also extracted, which induces the side lobe problem of rigid bodies. A parameter estimation method for rigid bodies after m-D removal is proposed, which can accurately re- cover rigid bodies and avoid the side lobe problem by only using m-D removal. Simulations are given to validate the effectiveness of the proposed method.

Application of Superresolution Techniques to Radar Imaging

Modern spectral estimation techniques (superresolution in technical jargon) have been applied to many fields of signal processing since many years[1][2]. Application to radar imaging, mainlyto ISAR (Inverse Synthetic Aperture Radar) is documented in some recent papers[3] to [6]. Applications have been attempted also to SAR (Synthetic Aperture Radar)[7][8]. In these fields the benefit ofspectral estimation reveals in a resolution beyond the Rayleigh limits set by compressed pulse andsynthetic aperture lengths. Furthermore very low sidelobes of point scatterer response are obtained.In this paper superresolution has been applied both to simulated stepped-frequency ISAR dataand to real ERS-1 SAR data; the achieved results are encouraging and suggest a more extensivepractical application of the technique. The paper is organized in two parts. In the first we have applied the autoregressive (AR) and the minimum variance (MV)-Capon methods to improve therange resolution of simulated ISAR data. In the second part we have conceived an upgraded versionof spectral analysis (SPECAN) processing to obtain a SAR image of better quality. The method hasbeen tested on recorded live data of ERS-1 mission.

Design of synthetic aperture radar low-intercept radio frequency stealth

Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out from two levels.In the first level,the maximum low-intercept range equation of the conventional SAR system is deduced firstly,and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced.In the second level,the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation.Finally,the main technical characteristics of the lowintercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.

Edge Detection of River in SAR Image Based on Contourlet Modulus Maxima and Improved Mathematical Morphology

To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

Improved HHT and its application in narrowband radar imaging for precession cone-shaped targets

Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficiencies for extracting microDoppler information in practical applications, which leads to blurring of the image. A new narrowband radar imaging algorithm for the precession cone-shaped targets is proposed. The instantaneous frequency of each scattering point is gained by using the improved Hilbert-Huang transform, then the positions of scattering points in the parameter domain are reconstructed. Numerical simulation and experiment results confirm the effectiveness and high precision of the proposed algorithm.