Target Detection In Synthetic Aperture Radar Images Research Articles

Enhanced Detection Method for Small and Occluded Targets in Large-Scene Synthetic Aperture Radar Images

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multiple scenes and multiple sizes, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head

Synthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is proposed to simulate the SAR images of non-cooperative aircraft targets under different conditions. Combining the iterative physical optics and the Kirchhoff approximation, the scattering coefficient of each facet on the target and rough surface can be obtained. Then, the radar echo signal of an aircraft target above a rough surface environment can be generated, and the SAR images can be simulated under different conditions. Finally, through the simulation experiments, a dataset of typical non-cooperative targets can be established. Combining the YOLOv5 network with the convolutional block attention module (CBAM) and another detection head, a SAR image target detection model based on the established dataset is realized. Compared with other YOLO series detectors, the simulation results show a significant improvement in precision. Moreover, the automatic target recognition system presented in this paper can provide a reference for the detection and recognition of non-cooperative aircraft targets and has great practical application in situational awareness of battlefield conditions.

Attention feature fusion awareness network for vehicle target detection in SAR images

ABSTRACT Synthetic aperture radar (SAR) target detection plays a crucial role in military surveillance, earth observation, and disaster monitoring. With the development of deep learning (DL) and SAR imaging technology, numerous SAR target detection methods have been proposed and achieved better detection results. However, detecting different categories of SAR vehicle targets is still challenging due to the influence of coherent speckle noises and background clutter. This article presents a novel attention feature fusion awareness network (AFFNet) for vehicle target detection in SAR images. Specifically, we propose a multi-scale semantic attention (MSSA) module to obtain multi-scale and semantic features of target region; the variable multi-scale feature fusion (VMSFF) module is introduced to effectively fuse different feature information and alleviate target deformation interference by establishing feature correlation; the part feature awareness (PFA) module is used to obtain unique attribute of different vehicle targets to generate accurate anchor boxes. In addition, we design a candidate boundary box selection scheme, which can effectively adapt to SAR targets with different scales and categories. Overall, AFFNet is designed based on the SAR imaging mechanism and target physical feature information. To evaluate the performance of the proposed method, extensive experiments are conducted on the MSTAR dataset. The experiment results show that the proposed AFFNet obtains the mAP of 98.36 and 97.26 on standard operating conditions (SOCs) and extended operating conditions (EOCs), which is more efficient than the other state-of-the-art methods.

SAR Image Aircraft Target Recognition Based on Improved YOLOv5

Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (mAP) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5.

A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images

Synthetic aperture radar (SAR) can detect objects in various climate and weather conditions. Therefore, SAR images are widely used for maritime object detection in applications such as maritime transportation safety and fishery law enforcement. However, nearshore ship targets in SAR images are often affected by background clutter, resulting in a low detection rate, high false alarm rate, and high missed detection rate, especially for small-scale ship targets. To address this problem, in this paper, we propose a novel deep learning network with deformable convolution and attention mechanisms to improve the Feature Pyramid Network (FPN) model for nearshore ship target detection in SAR images with complex backgrounds. The proposed model uses a deformable convolutional neural network in the feature extraction network to adapt the convolution position to the target sampling point, enhancing the feature extraction ability of the target, and improving the detection rate of the ship target against the complex background. Moreover, this model uses a channel attention mechanism to capture the feature dependencies between different channel graphs in the feature extraction network and reduce the false detection rate. The designed experiments on a public SAR image ship dataset show that our model achieves 87.9% detection accuracy for complex scenes and 95.1% detection accuracy for small-scale ship targets. A quantitative comparison of the proposed model with several classical and recently developed deep learning models on the same SAR images dataset demonstrated the superior performance of the proposed method over other models.

Grid-guided localization network based on the spatial attention mechanism for synthetic aperture radar ship detection

Synthetic aperture radar (SAR) has become an important part of ship detection due to its all-day all-weather characteristics. Ship monitoring is important for coastal traffic control and territorial safety. Ship detection requires high accuracy, but the speed of ship detection is also an important parameter in making rapid decisions, such as those in maritime rescue and military strategy. However, fast networks tend to be less accurate. To address this problem, we propose an efficient localization method based on a spatial attention mechanism. This mechanism uses grid-guided localization instead of a frame regression mechanism, which makes the network faster but also lowers the accuracy to a level insufficient for direct application to SAR image target detection. A tested and selected spatial attention mechanism improves the detection accuracy while guaranteeing its speed. It is shown that four factors affect the design of the spatial attention mechanism, namely query and key content, query content and relative position, key content, and relative position. These factors are ordered, and the group with the best precision is added to the proposed network. The proposed network can achieve a good detection effect for sparsely distributed targets in SAR images, and the proposed algorithm can achieve AP50 ∶ 75, AP50, AP75, and APS values of 71.3%, 96.9%, 86.6%, and 72.1%, respectively, on the SAR ship detection dataset.

An Anchor-Free Detection Algorithm for SAR Ship Targets with Deep Saliency Representation

Target detection in synthetic aperture radar (SAR) images has a wide range of applications in military and civilian fields. However, for engineering applications involving edge deployment, it is difficult to find a suitable balance of accuracy and speed for anchor-based SAR image target detection algorithms. Thus, an anchor-free detection algorithm for SAR ship targets with deep saliency representation, called SRDet, is proposed in this paper to improve SAR ship detection performance against complex backgrounds. First, we design a data enhancement method considering semantic relationships. Second, the state-of-the-art anchor-free target detection framework CenterNet2 is used as a benchmark, and a new feature-enhancing lightweight backbone, called LWBackbone, is designed to reduce the number of model parameters while effectively extracting the salient features of SAR targets. Additionally, a new mixed-domain attention mechanism, called CNAM, is proposed to effectively suppress interference from complex land backgrounds and highlight the target area. Finally, we construct a receptive-field-enhanced detection head module, called RFEHead, to improve the multiscale perception performance of the detection head. Experimental results based on three large-scale SAR target detection datasets, SSDD, HRSID and SAR-ship-dataset, show that our algorithm achieves a better balance between ship target detection accuracy and speed and exhibits excellent generalization performance.

A Survey of SAR Image Target Detection Based on Convolutional Neural Networks

Synthetic Aperture Radar (SAR) target detection is a significant research direction in radar information processing. Aiming at the poor robustness and low detection accuracy of traditional detection algorithms, SAR image target detection based on the Convolutional Neural Network (CNN) is reviewed in this paper. Firstly, the traditional SAR image target detection algorithms are briefly discussed, and their limitations are pointed out. Secondly, the CNN’s network principle, basic structure, and development process in computer vision are introduced. Next, the SAR target detection based on CNN is emphatically analyzed, including some common data sets and image processing methods for SAR target detection. The research status of SAR image target detection based on CNN is summarized and compared in detail with traditional algorithms. Afterward, the challenges of SAR image target detection are discussed and future research is proposed. Finally, the whole article is summarized. By summarizing and analyzing prior research work, this paper is helpful for subsequent researchers to quickly recognize the current development status and identify the connections between various detection algorithms. Beyond that, this paper summarizes the problems and challenges confronting researchers in the future, and also points out the specific content of future research, which has certain guiding significance for promoting the progress of SAR image target detection.

SAR Image Target Detection based on Attention YOLOv4

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.

Feature Selection for SAR Target Discrimination and Efficient Two-Stage Detection Method

Feature-based target detection in synthetic aperture radar (SAR) images is required for monitoring situations where it is difficult to obtain a large amount of data, such as in tactical regions. Although many features have been studied for target detection in SAR images, their performance depends on the characteristics of the images, and both efficiency and performance deteriorate when the features are used indiscriminately. In this study, we propose a two-stage detection framework to ensure efficient and superior detection performance in TSX images, using previously studied features. The proposed method consists of two stages. The first stage uses simple features to eliminate misdetections. Next, the discrimination performance for the target and clutter of each feature is evaluated and those features suitable for the image are selected. In addition, the Karhunen–Loève (KL) transform reduces the redundancy of the selected features and maximizes discrimination performance. By applying the proposed method to actual TerraSAR-X (TSX) images, the majority of the identified clusters of false detections were excluded, and the target of interest could be distinguished.

Gaussian Function Fusing Fully Convolutional Network and Region Proposal-Based Network for Ship Target Detection in SAR Images

Recently, ship target detection in Synthetic aperture radar (SAR) images has become one of the current research hotspots and plays an important role in the real-time detection of sea regions. The traditional SAR ship detection methods usually consist of two modules, one module named land-sea segmentation for removing the complicated land regions, and one module named ship target detection for realizing fine ship detection. An algorithm combining the Unet-based land-sea segmentation method and improved Faster RCNN-based ship detection method is proposed in this paper. The residual convolution module is introduced into the Unet structure to deepen the network level and improve the feature representation ability. The K-means method is introduced in the Faster RCNN method to cluster the size and aspect ratio of ship targets, to improve the anchor frame design, and make it more suitable for our ship detection task. Meanwhile, a fine detection algorithm uses the Gaussian function to fuse the confidence value of sea-land segmentation results and the coarse detection results. The segmentation and detection results on the established segmentation dataset and detection dataset, respectively, demonstrate the effectiveness of our proposed segmentation methods and detection methods.

SII-Net: Spatial Information Integration Network for Small Target Detection in SAR Images

Ship detection based on synthetic aperture radar (SAR) images has made a breakthrough in recent years. However, small ships, which may be regarded as speckle noise, pose enormous challenges to the accurate detection of SAR images. In order to enhance the detection performance of small ships in SAR images, a novel detection method named a spatial information integration network (SII-Net) is proposed in this paper. First, a channel-location attention mechanism (CLAM) module which extracts position information along with two spatial directions is proposed to enhance the detection ability of the backbone network. Second, a high-level features enhancement module (HLEM) is customized to reduce the loss of small target location information in high-level features via using multiple pooling layers. Third, in the feature fusion stage, a refined branch is presented to distinguish the location information between the target and the surrounding region by highlighting the feature representation of the target. The public datasets LS-SSDD-v1.0, SSDD and SAR-Ship-Dataset are used to conduct ship detection tests. Extensive experiments show that the SII-Net outperforms state-of-the-art small target detectors and achieves the highest detection accuracy, especially when the target size is less than 30 pixels by 30 pixels.

SEFEPNet: Scale Expansion and Feature Enhancement Pyramid Network for SAR Aircraft Detection With Small Sample Dataset

Aircraft detection in synthetic aperture radar (SAR) images is still a challenging research task because of the insufficient public data, the difficulty of multiscale target detection, and the complexity of background interference. In this article, we construct a public SAR aircraft detection dataset (SADD) with complex background and interference objects to facilitate the research in SAR aircraft detection. Then, we propose the scale expansion and feature enhancement pyramid network as the SADD baseline. It uses a four-scale fusion method to combine the shallow position information with the deep semantic information, effectively adapting to the multiscale target detection in SAR images, significantly improving the detection effect of small targets. The feature enhancement pyramid structure is connected behind the backbone network to weaken the background texture and highlight the target to achieve feature enhancement, improving the ability to extract target features in complex backgrounds. Finally, to further improve the detection performance of the small-scale SAR aircraft dataset, we propose a domain adaptive transfer learning method. Experiments on SADD show that this method can significantly improve the recall rate and F1 score. At the same time, we find that the transfer effect of using homologous but different types of targets as the source domain is better than those of heterologous but same types of targets in SAR aircraft detection, which is instructive for future research.

Light-SAR-ShipNet (LSSNet): A Lightweight SAR Image Ship Detection System with Enhanced Deep Supervision

Applying deep learning methods to achieve synthetic aperture radar (SAR) image ship target detection has become a current hot research topic. However, most of the existing detection networks are designed based on optical images, so when they are directly applied to SAR image target detection, there will be the following two problems: 1) There are big differences between SAR image and optical image in imaging mechanism, geometric characteristics and radiation characteristics, etc, which makes the detection network have more redundant imformation; 2) Real-time performance and accuracy are both very important in SAR image target detection task, and applying a network designed based on optical image cannot balance the performance of the two well. In response to the above problems, this paper proposes a lightweight network LSSNet specially designed for SAR image ship detection task. In earlier layers, we use depthwise separable convolution instead of conventional convolution to design dense block; in deeper layers, stacked dense blocks with shortcuts are used to enhance deep supervision, and finally achieve high-speed and high-accuracy detection. This paper uses SSDD dataset as baseline for expeiment and results show that LSSNet have higher accurracy and detecition speed. Its lightweight structure will also help transplant to hardware devices in the future.

RETRACTED ARTICLE: Data measurement and aerobics online teaching based on SAR target detection network

Target detection technology is a complex multidisciplinary technology, including signal processing, image analysis, and depth calculation and other data processing methods, mainly used in the military field. Because synthetic aperture radar (SAR) images have the characteristics of multiple types and high variability, the development of SAR image detection technology in practical applications is relatively slow. Therefore, in this field, target detection in SAR images is a very challenging technical research. Based on the research on the domestic and foreign background of this detection technology, this paper systematically explained the calculation method of the target detection technology, and made practical and applied research on the online teaching of aerobics through various methods such as literature review and questionnaire survey. Aerobic fitness is a comprehensive exercise, aesthetics, and art course. It not only exercises the body and shows personal charm, but also cultivates students’ teamwork ability. Now more and more schools offer aerobics courses as elective courses for physical education, allowing students to strengthen exercise and improve their physical fitness while studying. But on the whole, the curriculum lacks diversification and the content lacks interest, which greatly affects students’ enthusiasm for learning. This article uses the literature method to consult a large number of materials, analyzes the basic principles of the online classroom teaching model, researches and analyzes the online classroom teaching model adopted by many scholars, and uses the expert interview method, according to the advice of experts and professors, combined with online teaching. The characteristics of online aerobics teaching methods and questionnaires suitable for this research are designed.

Ship Detection in SAR Images via Local Contrast of Fisher Vectors

Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms.

Saliency-Guided Single Shot Multibox Detector for Target Detection in SAR Images

The single shot multibox detector (SSD), a proposal-free method based on convolutional neural network (CNN), has recently been proposed for target detection and has found applications in synthetic aperture radar (SAR) images. Moreover, the saliency information reflected in the saliency map can highlight the target of interest while suppressing clutter, which is beneficial for better scene understanding. Therefore, in this article, we propose a saliency-guided SSD (S-SSD) for target detection in SAR images, in which we effectively integrate the saliency into the SSD network not only to suggest where to focus on but also to improve the representation capability in complex scenes. The proposed S-SSD contains two separated convolutional backbone subnetwork architectures, one with the original SAR image as input to extract features, and the other with the corresponding saliency map obtained from the modified Itti’s method as input to acquire refined saliency information under supervision. In addition, the dense connection structure, instead of the plain structure used in original SSD, is applied in the two convolutional backbone architectures to utilize multiscale information with fewer parameters. Then, for integrating saliency information to guide the network to emphasize informative regions, multilevel fusion modules are utilized to merge the two streams into a unified framework, thereby making the whole network end-to-end jointly trained. Finally, the convolutional predictors are used to predict targets. The experimental results on the miniSAR real data demonstrate that the proposed S-SSD can achieve better detection performance than state-of-the-art methods.

Target detection in SAR images using Bayesian Saliency and Morphological attribute profiles

The Synthetic Aperture Radar (SAR) images have become a considerable and vital application in the optical satellite images due to their ability of function in all kinds of weather situations. In synthetic aperture radar (SAR) images Target detection is a demanding task to check presence of targets in images and to exactly locate that target. This paper propose a process for the detection of automatic target in SAR images which has three different steps, that is super pixel segmentation, Morphological attribute profiles and Bayesian Saliency Map. In first stage the SAR input image is then segmented into small regions by super pixels after that the clustering procedure is followed. In the second stage Bayesian saliency map is used to highpoint the clear objects including target and shadow. In the third stage the morphological attribute profile is used to suppress the shadow and highlight the target. The experiment results on MSTAR dataset are compared with some popular existing algorithms (Super pixel CFAR, local CFAR, PR, Global CFAR, MS, BS) which will proves that the proposed mechanism is effective and robust.

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar (SAR) has attracted considerable attention of researchers in national defense technology worldwide, owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR. However, due to strong speckle noise and low signal-to-noise ratio, it is difficult to extract representative features of target from SAR images, which greatly inhibits the effectiveness of traditional methods. In order to address the above problems, a framework called contextual rotation region-based convolutional neural network (RCNN) with multilayer fusion is proposed in this paper. Specifically, aimed to enable RCNN to perform target detection in large scene SAR images efficiently, maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN. Instead of using the highest-layer output for proposal generation and target detection, fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy. Then, we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region. Furthermore, shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately. Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.

Feature clustering based discrimination of ship targets for SAR images

Synthetic aperture radar (SAR) is one of the most important and widely-used tools of large scale marine surveillance. Constant false alarm rate is tailored for maritime metallic target detection in SAR images. However, it is very difficult to distinguish ship targets from other metallic targets. Some manual features have been designed to discriminate ship and non-ship target. These manual features often overfit some scenarios but fail to other situations. Recently, deep convolutional networks have achieved impressive success in optical image classification, which encourages scholars of remote sensing society to leverage this powerful tool to extract robust features of SAR ship targets. This study adopts VGG-16 to extract features of ship and non-ship targets. Then, unsupervised clustering is conducted to group ship and non-ship targets, respectively, using the extracted features, which can effectively tackle intraspecies variants of ship and non-ship targets. Finally, these representative feature vectors are utilised to differentiate ship and non-ship targets of other scenarios. The proposed algorithms are extensively tested undertaken on 11 ALOS-2 SAR images, which demonstrate good performance of the methods.