Survey on Fast CFAR Detection Algorithms for SAR Image Targets

A bilateral constant false alarm rate (CFAR) algorithm for ship detection in synthetic aperture radar (SAR) images is proposed in this letter. Compared to the standard CFAR algorithm, the proposed algorithm can reduce the influence of SAR ambiguities and sea clutter, by means of a combination of the intensity distribution and the spatial distribution of SAR images. The spatial distribution plays an equally important role as the intensity distribution. It is estimated before ship detection by a new kernel density estimation algorithm proposed in this letter. The experimental results of typical SAR images show that the algorithm is effective.

Target detection for synthetic aperture radar (SAR) images has great influence on the successive discrimination based on the target regions. However, as a pixel-based method, the traditional constant false alarm rate (CFAR) detection could not work well for the ship target detection problem of multiple ship targets with different sizes in a SAR image, which is referred to as the multiscale situation. Moreover, it needs to use the clustering method on the pixel-level detection results to obtain the accurate target regions, which may merge two or more different targets into a target region. In this letter, a modified CFAR based on object proposals is proposed. We use the object proposal generator to generate a small set of object proposals with different sizes, and then use the proposal-based CFAR detector, where the extracted object proposals are regarded as the guard windows instead of setting fixed guard window, to detect the true positive object proposals. By introducing the object proposals as the variable guard windows in the CFAR detector, the proposed algorithm could gain good detection performance in the multiscale situation, since the missed detection resulting from the big differences between the sizes of the fixed guard window and ship targets can be avoided. Meanwhile, the proposed method can directly obtain the accurate target regions. The effectiveness of the proposed algorithm is verified using the measured SAR data.

The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm.

Target detection in the multiscale situation where there exit multiple ship targets with different sizes is a challenging task due to the mismatch of the sizes of ship targets and fixed windows. A new constant false alarm rate (CFAR) algorithm based on variable window for ship target detection in SAR images is proposed. First, the multiscale local contrast measure is introduced to estimate the ship target size without any prior knowledge about ships. Second, the size of neighborhood area is adaptively set and a transform algorithm is designed to enhance the contrast between targets and background. Finally, CFAR detection is implemented by adopting variable window to gain the accurate ship targets. Experimental results indicate that the proposed algorithm has better performance compared with other CFAR detection algorithms.

As the first step of automatic image interpretation systems, automatic detection of the targets should be accurate and fast. Constant False Alarm Rate (CFAR) is the most popular target detection framework for Synthetic Aperture Radar (SAR) images. For CFAR, modeling of the clutter is crucial since the decision threshold is calculated based on this model. We have developed a new target detection approach in which clutter is modeled using a Rayleigh mixture model that fits well to a variety of high-resolution SAR imagery. For computational efficiency, we use summed area tables (SAT) for computing background statistics. The resulting approach, called RmSAT-CFAR, is a promising general-purpose SAR target detection tool. This paper describes the open-source software for RmSAT-CFAR. Details of RmSAT-CFAR is published in the study named Fast Target Detection in Radar Images using Rayleigh Mixtures and Summed Area Tables. In addition to Rayleigh mixture and SATs, the software also uses tiling and parallelization to obtain faster and scalable results. This software repository also contains open source implementations for following algorithms: (a) Cell Averaging CFAR (CA-CFAR), (b) Automatic Censored CFAR (AC-CFAR), and (c) Adaptive and Fast CFAR (AAF-CFAR).

We address the application of model-supported exploitation techniques to synthetic aperture radar (SAR) imagery. The emphasis is on monitoring SAR imagery using wide area 2D and/or 3D site models along with contextual information. We consider here the following tasks useful in monitoring: (a) site model construction using segmentation and labeling techniques, (b) target detection, (c) target classification and indexing, and (d) SAR image-site model registration. The 2-D wide area site models used here for SAR image exploitation differ from typical site models developed for RADIUS applications, in that they do not model specific facilities, but constitute wide area site models of cultural features such as urban clutter areas, roads, clearings, fields, etc. These models may be derived directly from existing site models, possibly constructed from electro-optical (EO) observations. When such models are not available, a set of segmentation and labeling techniques described here can be used for the construction of 2D site models. The use of models can potentially yield critical information which can disambiguate target signatures in SAR images. We address registration of SAR and EO images to a common site model. Specific derivations are given for the case of registration within the RCDE platform. We suggest a constant false alarm rate (CFAR) detection scheme and a topographic primal sketch (TPS) based classification scheme for monitoring target occurrences in SAR images. The TPS of an observed target is matched against candidate targets TPSs synthesized for the preferred target orientation, inferred from context (e.g. road or parking lot targets). Experimental results on real and synthetic SAR images are provided.

In high-resolution synthetic aperture radar (SAR) images, the target size may be much larger than the resolution of SAR and the background clutter statistics are generally not Gaussian-distributed. But traditional constant false alarm rate (CFAR) detection algorithm, which based on the Gaussian-distributed model, performs only one test cell at a time and ignores the mutuality information with other cells in the target, so the performance of the CFAR detection algorithm can be substantially degraded. In the paper, an extend target detection method based on the generalized compound model is proposed. Unlike traditional CFAR detection method which distinguish test cell from the background only on the basis of energy contrast, the extend target detection is sensitive to both the contrast and the distribution of the target. We demonstrate the improved performance using the extend target detection method in the real high-resolution SAR images.

The region of interest (ROI) contains a potential target in synthetic aperture radar (SAR) image. Picking out ROI chips before target recognizing will accelerate the whole automatic target recognition (ATR) system. This paper improves the traditional method to obtain ROI chips through constant false alarm rate (CFAR) algorithm. With the help of integral image, we reduce redundant calculation in traditional cell average CFAR (CA-CFAR) to increase its computing speed, which is denoted as the Integral Image CFAR (II-CFAR). Finally we compare the ROI extraction results of the traditional CA-CFAR and the proposed II-CFAR on the same real SAR image. The results show that the II-CFAR detector can obtain the same detection performance with the CA-CFAR detector but with a much shorter running time.

In this paper, a new automatic and adaptive aircraft target detection algorithm in high-resolution airport synthetic aperture radar (SAR) images is proposed. Firstly, region segmentation is used to detect the apron area in the images, which provides the potential area where aircrafts may exist and reduce the search range. Secondly, upon the apron area the pre-segmentation is taken to label the possible target points. Thirdly, the constant false alarm rate (CFAR) detector is improved to cope with multi-target detection situation. The clutter pixels in the sliding detection window will be removed automatically based on pre-segmentation result. As a result, more structural features of the targets are preserved. At last, in order to eliminate the detected false targets and solve the problem that the same target is divided into several disconnected areas, a new joint algorithm based on the area recognition factors and distance cluster is presented. The real airborne SAR image data of some airport is used to verify this target detection algorithm, and the result indicates that this algorithm can detect the aircraft target precisely and decrease the false alarm rate.

In recent years, the rapid development of Deep Learning (DL) has provided a new method for ship detection in Synthetic Aperture Radar (SAR) images. However, there are still four challenges in this task. (1) The ship targets in SAR images are very sparse. A large number of unnecessary anchor boxes may be generated on the feature map when using traditional anchor-based detection models, which could greatly increase the amount of computation and make it difficult to achieve real-time rapid detection. (2) The size of the ship targets in SAR images is relatively small. Most of the detection methods have poor performance on small ships in large scenes. (3) The terrestrial background in SAR images is very complicated. Ship targets are susceptible to interference from complex backgrounds, and there are serious false detections and missed detections. (4) The ship targets in SAR images are characterized by a large aspect ratio, arbitrary direction and dense arrangement. Traditional horizontal box detection can cause non-target areas to interfere with the extraction of ship features, and it is difficult to accurately express the length, width and axial information of ship targets. To solve these problems, we propose an effective lightweight anchor-free detector called R-Centernet+ in the paper. Its features are as follows: the Convolutional Block Attention Module (CBAM) is introduced to the backbone network to improve the focusing ability on small ships; the Foreground Enhance Module (FEM) is used to introduce foreground information to reduce the interference of the complex background; the detection head that can output the ship angle map is designed to realize the rotation detection of ship targets. To verify the validity of the proposed model in this paper, experiments are performed on two public SAR image datasets, i.e., SAR Ship Detection Dataset (SSDD) and AIR-SARShip. The results show that the proposed R-Centernet+ detector can detect both inshore and offshore ships with higher accuracy than traditional models with an average precision of 95.11% on SSDD and 84.89% on AIR-SARShip, and the detection speed is quite fast with 33 frames per second.

This paper proposes an Synthetic Aperture Radar (SAR) imaging and detection model of multiple targets at the maritime scene. The sea surface sample is generated according to the composite rough surface theory. The SAR imaging model is constructed based on a hybrid EM calculation approach with the fast ray tracing strategy and the modified facet Small Slope Approximation (SSA) solution. Numerical simulations calculate the EM scattering and the SAR imaging of the multiple cone targets above the sea surface, with the scattering mechanisms analyzed and discussed. The SAR imaging datasets are then set up by the SAR image simulations. A modified YOLOv7 neural network with the Spatial Pyramid Pooling Fast Connected Spatial Pyramid Convolution (SPPFCSPC) module, Convolutional Block Attention Module (CBAM), modified Feature Pyramid Network (FPN) structure and extra detection head is developed. In the training process on our constructed SAR datasets, the precision rate, recall rate, mAP@0.5 and mAP@0.5:0.95 are 97.46%, 90.08%, 92.91% and 91.98%, respectively, after 300 rounds of training. The detection results show that the modified YOLOv7 has a good performance in selecting the targets out of the complex sea surface and multipath interference background.

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

Constant false alarm rate (CFAR) algorithms are widely used in radar signal processing for object detection in cluttered and noisy environments and their fast hardware implementation is required in many of the real time applications. For that purpose, a parametrizable and runtime reconfigurable generator of CFAR detectors, featuring fully streaming I/O data interface, is captured inside Chisel hardware design language. Generator provides up to seven different CFAR algorithms available to choose from in compile time and throughout runtime configurability. Besides the algorithm choice, a wide range of settings, such as I/O data type and bit-widths, reference and guard window sizes, linear or logarithmic processing modes, edge handling methods, as well as some implementation specific parameters, are provided, hence enabling quick and efficient design space exploration. Several generator instances are tested and verified on a commercially-available FPGA board in conjunction with off-the-shelf radar transceivers thus proving that instances from the proposed peak detector generator can be effectively used whenever low latency processing performance is mandatory.

Inshore ship detection in synthetic aperture radar (SAR) images is a challenging task. We present an inshore ship detection method based on the characteristics of inshore ships. We first use the Markov random field (MRF) method to segment water and land, and then extract the feature points of inshore ships using polygonal approximation. Following this, we propose new rules for inshore ship extraction and use these rules to separate inshore ships from the land in binary images. Finally, we utilize the adaptive background window (ABW) to complete the clutter statistic and successfully detect inshore ships using a constant false alarm rate (CFAR) detector with ABW and G0 distribution. Experimental results using SAR images show that our method is more accurate than traditional CFAR detection based on K-distribution (K-CFAR), given the same CFAR, and that the quality of the image obtained through our method is higher than that of the traditional K-CFAR detection method by a factor of 0.165. Our method accurately locates and detects inshore ships in complicated environments and thus is more practical for inshore ship detection.

Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.