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With the maturity of synthetic aperture radar (SAR) technology, the problem of imbalanced data has gradually emerged. This problem makes it difficult for the automatic target recognition (ATR) model to properly learn the classification boundaries of majority and minority category target samples. In this article, we propose an ATR model with new architecture, called the cost-sensitive awareness-based automatic target recognition (CA-ATR) model, which provides an effective way of solving the problem of imbalanced data. Aimed at the two issues caused by imbalanced data on ATR models, the proposed method solves the problems from both the data and algorithm levels. At the data level, CA-ATR avoids adverse correlations among the target samples through different oversampling methods. By making the ATR model cost-sensitive, the proposed method also avoids the empirical risk preference of the ATR model for majority category target samples at the algorithm-level. At the same time, CA-ATR can autonomously learn different cost-sensitive awareness from different imbalanced data sets. The awareness enables the ATR model to more accurately learn the classification boundaries between target samples that belong in different categories. Several experimental results show the superiority of the proposed approach based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Compared with other imbalanced learning methods, the proposed method is able to solve different types of imbalanced data problems.

The constant false-alarm-rate (CFAR) target detection is an important research direction for synthetic aperture radar (SAR) image application. The traditional pixel-level CFAR method has great shortcomings in eliminating the false-alarm targets and keeping the complete information of the targets. Thus, the superpixel-level CFAR has become an important topic in research in recent years. However, the current superpixel-level CFAR methods have not considered or built a superpixel clutter-distribution model. Therefore, an improved CFAR based on superpixel modeling is proposed in this letter. A superpixel-level compound Gamma distribution was built to describe the clutter statistical model, which can obtain a more accurate fitting than the pixel-level Gamma distribution. The experiments on the SAR images verified that the proposed method can effectively suppress the influence of the background clutter to reduce the false alarms and can keep the complete shape information of the targets. As a result, the proposed method outperforms the traditional pixel-level CFAR and the current superpixel-level CFAR methods.

Ship target detection using large-scale synthetic aperture radar (SAR) images has important application in military and civilian fields. However, ship targets are difficult to distinguish from the surrounding background and many false alarms can occur due to the influence of land area. False alarms always occur with ship target detection because most of the area in large-scale SAR images are treated as background and clutter, and the ship targets are considered unevenly distributing small targets. To address these issues, a ship detection method in large-scale SAR images via CenterNet is proposed in this article. As an anchor-free method, CenterNet defines the target as a point, and the center point of the target is located through key point estimation, which can effectively avoid the missing detection of small targets. At the same time, the spatial shuffle-group enhance (SSE) attention module is introduced into CenterNet. Through SSE, the stronger semantic features are extracted while suppressing some noise to reduce false positives caused by inshore and inland interferences. The experiments on the public SAR-ship-data set show that the proposed method can detect all targets without missed detection with dense-docking targets. For the ship targets in large-scale SAR images from Sentinel 1, the proposed method can also detect targets near the shore and in the sea with high accuracy, which outperforms the methods like faster R-convolutional neural network (CNN), single-shot multibox detector (SSD), you only look once (YOLO), feature pyramid network (FPN), and their variations.

Noncontact gesture recognition is gradually being applied to emerging applications, such as smart cars and smart phones. Negative latency gesture recognition (recognition before a gesture is finished) is desirable due to the instantaneous feedback. However, it is difficult for existing methods to achieve a high precision and negative latency gesture recognition. A fragment can provide too few features to directly identify all gestures well. By observing a large number of existing gesture sets and people’s daily operating habits, we found that some high frequency used gestures are similar. To the best of our knowledge, it is the first time to redivide the gestures into two subsets according to their movement physical states. We divided the gestures with different shapes or motion states into a parent-class subset, and further divided each pair of parent-class gestures to obtain a child-class subset. In order to achieve a better tradeoff between the high-precision and negative latency, an approach of motion pattern and behavior intention (MPBI) is proposed. Taking full advantage of the characteristics of each subset, MPBI includes two models. First, pattern model coarsely classify the parent-class gestures by a convolutional network, and then intention model further classifies child-class gestures according to their opposite motion direction. MPBI is evaluated on a 340-GHz terahertz radar. With the advantage of its accurate ranging, intention model can recognize child-class gestures directly without training. MPBI is evaluated on 12 gestures and achieves a recognition accuracy of 94.13%, which only needs a 0.033-s gesture fragment as an input sample.

When adding new tasks/classes in an incremental learning scenario, the previous recognition capabilities trained on the previous training data can be lost. In the real-life application of automatic target recognition (ATR), part of the previous samples may be able to be used. Most incremental learning methods have not considered how to save the previous key samples. In this article, the class boundary exemplar selection-based incremental learning (CBesIL) is proposed to save the previous recognition capabilities in the form of the class boundary exemplars. For exemplar selection, the class boundary selection method based on local geometrical and statistical information is proposed. And when adding new classes continually, a class-boundary-based data reconstruction method is introduced to update the exemplar set. Thus, when adding new classes, the previous class boundaries could be kept complete. Experimental results demonstrate that the proposed CBesIL outperforms the other state of the art on the accuracy of multiclass recognition and class-incremental recognition.

Under the framework of a supervised learning-based automatic target recognition (ATR) approach, recognition performance is primarily dependent on the amount of training samples. However, shortage in training samples is a consistent issue for ATR. In this article, we propose a new image to image generation method, called label-directed generative adversarial networks (LDGANs), which will provide labeled samples to be used for recognition model training. We define an entirely new loss function for the LDGAN, which utilizes the Wasserstein distance to replace the original distance measurement of the conventional generative adversarial networks (GANs), thus efficiently avoiding the collapse mode problem. The label information is also added to the loss function of the LDGAN to avoid generating a large number of unlabeled target images. More importantly, the proposed method also makes corresponding changes to the network architecture regarding the new GANs. At the same time, the detailed algorithm about the LDGAN is also introduced in this article to deal with the issue that characteristically GANs are not easy to train. Based on comparisons with other directed generation methods, the experimental results show comparative results of several types of generated images in statistical features, gradient features, classic features of synthetic aperture radar (SAR) targets and the independence from the real image. While demonstrating that the images generated by the LDGAN produced better results using the assumptions of independent and identical distribution, the experiment also explores the performance of the generated image in the ATR. A comparison of these experimental results demonstrates a better way to use the generated image for ATR. The experimental results also prove that the proposed method does have the ability to supplement information for ATR when the training sample information is insufficient.

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods easily result in inaccurate detection due to the complex scenes and multiplicative speckle noise. Therefore, airport detection from SAR images is still a challenging task. In order to limit the influence of unnecessary and attractive details and noise, we propose a multi-layer abstraction saliency model for airport detection in SAR images in this paper. Specifically, we first obtain airport support regions and superpixels in the first layer. According to the dis-similarity between foreground and background superpixels, airport components are explored by iterative refinement for each airport support region in the second layer. In the third layer, airport adobes are produced by clustering. Based on the characteristics of an airport in SAR images, we propose three saliency cues, including local contrast (LC), adobe deformation (AD), and global uniqueness (GU), to obtain adobe-level saliency. Furthermore, we assign saliency to each pixel by Bayesian inference. Finally, we can explore airport location using integrated saliency map. The proposed approach is tested on an airport data set collected from Gaofen-3, TerraSAR, and RadarSat. Our method achieves 88.89% detection rate. The experimental results demonstrate that the proposed algorithm is effective and outperforms the previously airport detection methods. The code will be available at https://github.com/NengyuanLiu/MyAirportSaliency .

Synthetic aperture radar (SAR) is an active microwave imaging sensor with the capability of working in all-weather, all-day to provide high-resolution SAR images. Recently, SAR images have been widely used in civilian and military fields, such as ship detection. The scales of different ships vary in SAR images, especially for small-scale ships, which only occupy few pixels and have lower contrast. Compared with large-scale ships, the current ship detection methods are insensitive to small-scale ships. Therefore, the ship detection methods are facing difficulties with multi-scale ship detection in SAR images. A novel multi-scale ship detection method based on a dense attention pyramid network (DAPN) in SAR images is proposed in this paper. The DAPN adopts a pyramid structure, which densely connects convolutional block attention module (CBAM) to each concatenated feature map from top to bottom of the pyramid network. In this way, abundant features containing resolution and semantic information are extracted for multi-scale ship detection while refining concatenated feature maps to highlight salient features for specific scales by CBAM. Then, the salient features are integrated with global unblurred features to improve accuracy effectively in SAR images. Finally, the fused feature maps are fed to the detection network to obtain the final detection results. Experiments on the data set of SAR ship detection data set (SSDD) including multi-scale ships in various SAR images show that the proposed method can detect multi-scale ships in different scenes of SAR images with extremely high accuracy and outperforms other ship detection methods implemented on SSDD.

Incremental learning methods update the existing model with new knowledge when the target data increase continuously. Open set recognition (OSR) algorithms provide classifiers with a rejection option so that the new untrained target type is identified. In this paper, an open set incremental learning method is introduced for automatic target recognition, which is able to recognize and learn the new unknown classes continually. The proposed method, open set model with incremental learning (OSmIL), is an ensemble classifier so it is able to be updated only by the new data. For saving the computational time and storage source, a new exemplar selection method is introduced for model simplifying. Edge samples are selected to cover training classes; as a result, the model size is deduced and controlled. Moreover, because extreme value theory (EVT) is suitable to fit a classification model that includes open space risk, the decision function based on EVT makes an open set classifier for identifying the new classes. Experimental results demonstrate that the proposed OSmIL outperforms the other state of the arts on the accuracy of multiclass OSR. And OSmIL can maintain good accuracy and efficiency in the incremental learning experiment set.

Convolutional neural networks (CNNs) have been widely used in synthetic aperture radar (SAR) target recognition. Traditional CNNs suffer from expensive computation and high memory consumption, impeding their deployment in real-time recognition systems of SAR sensors, as these systems have low memory resources and low speed of calculation. In this paper, a micro CNN (MCNN) for real-time SAR recognition system is proposed. The proposed MCNN has only two layers, and it is compressed from a deep convolutional neural network (DCNN) with 18 layers by a novel knowledge distillation algorithm called gradual distillation. MCNN is a ternary network, and all its weights are either -1 or 1 or 0. Following a student-teacher paradigm, the DCNN is the teacher network and MCNN is its student network. The gradual distillation makes MCNN a better learning route than traditional knowledge distillation. The experiments on the MSTAR dataset show that the proposed MCNN can obtain a high recognition rate which is almost the same as the DCNN. However, compared with the DCNN, the memory footprint of the proposed MCNN is compressed 177 times, and the calculated amount is 12.8 times less, which means that the proposed MCNN can obtain better performance with the smaller network.

As a mission-critical sensor, SAR has been applied in environmental monitoring and battlefield surveillance; moreover, SAR target recognition is one of the most important applications of SAR technology. However, in practical applications, the number of samples available for training is relatively small, so the SAR target recognition can be regarded as a small sample recognition problem. One of the main directions to solve the small sample recognition problem is to realize the data augmentation. Therefore, a SAR image data augmentation method via Generative Adversarial Nets (GAN) is proposed in this paper. The method uses Wasserstein GAN with a gradient penalty (WGAN-GP) to generate new samples based on existing SAR data, which can augment the sample number in training dataset. Meanwhile, the sample selection filters are designed to extract the generated samples with high quality and specific azimuth, which can avoid the randomness of the data augmentation, and improve the quality of the newly generated training samples. The experiments based on MSTAR data show that, for three-class recognition problem, when the training sample is only 108, the proposed method can improve the recognition rate from 79% to 91.6%; and for ten-class recognition problem, when the training sample is only 360, the proposed method can improve the recognition rate from 57.48% to 79.59%. Compared with the traditional data linear generation method, the proposed method shows significant improvement on the quantity and quality of the training samples, and can effectively solve the problem of the small sample recognition.

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods locate airports by connecting pairs of line segments or directly applying saliency analysis to an entire SAR image. These methods are either time-consuming or can easily result in false detection. Considering these issues, a method using line segment grouping and saliency analysis is proposed in this letter. First, line segments are obtained via an improved line segment detector (LSD). After line segment grouping, airport support regions are extracted. Then, selective nonmaximum suppression is proposed to obtain potential airport regions. Finally, airport regions are located by false alarm control and saliency analysis. Experiments on large-scale SAR images prove that our proposed algorithm has a better performance and higher efficiency in airport detection compared with traditional methods.