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.

Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images

The active recognition of interesting targets has been a vital issue for synthetic aperture radar (SAR) systems. The SAR recognition methods are mainly grouped as follows: extracting image features from the target amplitude image or matching the testing samples with the template ones according to the scattering centers extracted from the target complex data. For amplitude image-based methods, convolutional neural networks (CNNs) achieve nearly the highest accuracy for images acquired under standard operating conditions (SOCs), while scattering center feature-based methods achieve steady performance for images acquired under extended operating conditions (EOCs). To achieve target recognition with good performance under both SOCs and EOCs, a feature fusion framework (FEC) based on scattering center features and deep CNN features is proposed for the first time. For the scattering center features, we first extract the attributed scattering centers (ASCs) from the input SAR complex data, then we construct a bag of visual words from these scattering centers, and finally, we transform the extracted parameter sets into feature vectors with the $k$ -means. For the CNN, we propose a modified VGGNet, which can not only extract powerful features from amplitude images but also achieve state-of-the-art recognition accuracy. For the feature fusion, discrimination correlation analysis (DCA) is introduced to the FEC framework, which not only maximizes the correlation between the CNN and ASCs but also decorrelates the features belonging to different categories within each feature set. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) database demonstrate that the proposed FEC achieves superior effectiveness and robustness under both SOCs and EOCs.

FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features

Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a challenging problem in the case of complex environments, especially inshore and offshore scenes. Nowadays, the existing methods of SAR ship detection mainly use low-resolution representations obtained by classification networks or recover high-resolution representations from low-resolution representations in SAR images. As the representation learning is characterized by low resolution and the huge loss of resolution makes it difficult to obtain accurate prediction results in spatial accuracy; therefore, these networks are not suitable to ship detection of region-level. In this paper, a novel ship detection method based on a high-resolution ship detection network (HR-SDNet) for high-resolution SAR imagery is proposed. The HR-SDNet adopts a novel high-resolution feature pyramid network (HRFPN) to take full advantage of the feature maps of high-resolution and low-resolution convolutions for SAR image ship detection. In this scheme, the HRFPN connects high-to-low resolution subnetworks in parallel and can maintain high resolution. Next, the Soft Non-Maximum Suppression (Soft-NMS) is used to improve the performance of the NMS, thereby improving the detection performance of the dense ships. Then, we introduce the Microsoft Common Objects in Context (COCO) evaluation metrics, which provides not only the higher quality evaluation metrics average precision (AP) for more accurate bounding box regression, but also the evaluation metrics for small, medium and large targets, so as to precisely evaluate the detection performance of our method. Finally, the experimental results on the SAR ship detection dataset (SSDD) and TerraSAR-X high-resolution images reveal that (1) our approach based on the HRFPN has superior detection performance for both inshore and offshore scenes of the high-resolution SAR imagery, which achieves nearly 4.3% performance gains compared to feature pyramid network (FPN) in inshore scenes, thus proving its effectiveness; (2) compared with the existing algorithms, our approach is more accurate and robust for ship detection of high-resolution SAR imagery, especially inshore and offshore scenes; (3) with the Soft-NMS algorithm, our network performs better, which achieves nearly 1% performance gains in terms of AP; (4) the COCO evaluation metrics are effective for SAR image ship detection; (5) the displayed thresholds within a certain range have a significant impact on the robustness of ship detectors.

Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet

Automatic target recognition (ATR) for military applications is one of the core processes toward enhancing intelligence and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this article surveys and assesses current SAR ATR algorithms that employ the most popular dataset for the SAR domain, namely the moving and stationary target acquisition and recognition (MSTAR) dataset. Specifically, we perform a direct comparison between current SAR ATR methods and highlight the strengths and weaknesses of each technique under both standard and extended operational conditions. Additionally, despite MSTAR being the standard SAR ATR benchmarking dataset, we also highlight its weaknesses and suggest future research directions.

Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey

Compared with optical images, polarimetric synthetic aperture radar (PolSAR) images usually maintain lower resolution. Ship targets in PolSAR images have fewer pixels than those in optical images. Therefore, architectures such as Faster R-CNN and its variations that focus more on target pixels may fail for PolSAR images. Due to limited data and the simple geometric structures of small ship targets, the R-CNN framework with a large neural network as its backbone easily overfits the training set. In this article, we propose a novel lightweight patch-to-pixel convolutional neural network (P2P-CNN) for ship detection via PolSAR images. P2P-CNN focuses on both the target and its surroundings. A patch of proper size that contains both a target and its surroundings is used as the input of the neural network to determine whether the pixel in the center of the patch belongs to the target. To utilize contextual semantic information at all scales, all feature maps from the top down are combined to improve the final result. Instead of conventional convolution, dilated convolutions are used in the proposed neural network to exponentially expand receptive fields without adding any model parameters. The proposed approach and comparative methods are tested and compared on PolSAR images in various environments under varying image resolution, target size, sea condition, sensor type, etc. The experimental results demonstrate that the proposed approach outperforms all the compared methods.

A Patch-to-Pixel Convolutional Neural Network for Small Ship Detection With PolSAR Images

Target discrimination has been one of the hottest issues in the interpretation of synthetic aperture radar (SAR) images. However, the presence of speckle noise and the absence of robust features make SAR discrimination difficult to deal with. Recently, convolutional neural network has obtained state-of-the-art results in pattern recognition. In this letter, we propose a target discrimination framework that jointly uses intensity and edge information of SAR images. This framework contains three parts, namely, feature extraction block, feature fusion block, and final classification block. In addition, a novel feature fusion method that can preserve the spatial relationship of different features is introduced. Experimental results on the miniSAR data demonstrate the effectiveness of our method.

Feature-Fused SAR Target Discrimination Using Multiple Convolutional Neural Networks

To detect ships robustly and automatically in monitoring the marine areas, polarimetric synthetic aperture radar imagery is more and more important. In this letter, three superpixel-level dissimilarity measures are developed to enhance the contrast between ship targets and sea clutter, which are then used to construct an automatic detection algorithm. In the proposed method, multiscale superpixels are first generated. Second, the measurements between a certain superpixel and surrounding ones are calculated. The dissimilarity measures are then transformed from the superpixel level to the pixel level. Third, kernel Fisher discriminant analysis is utilized to improve the separability between ship targets and clutter. Finally, linear support vector machine classifier is utilized to complete the detection automatically. Experiments on the synthetic and real data demonstrate that the proposed method is effective for ship detection with only few false alarms existing, especially under the low signal-to-clutter ratio.

A Novel Automatic PolSAR Ship Detection Method Based on Superpixel-Level Local Information Measurement

Synthetic aperture radar (SAR) images are especially susceptible to the target aspect angles. For the SAR target recognition, the lack of training data at different aspect angles inevitably deteriorates the performance. To solve the problem, this letter introduces an angular rotation generative network (ARGN). It is actually an attribute-guided transfer learning method, and the shared attribute between the source and target domains is the target aspect angle. The aspect angle of the data for each type in the source domain covers in the range of 0°–360°, while the information of the aspect angle in the target domain is not complete. Assume that there is a mapping in the feature space between the two images of the same target under different aspect angles, and the mapping relation is learned from sufficient data in the source domain. Then, the mapping can also be applied in the target domain according to the idea of transfer learning. The learned knowledge contained in the feature space helps to improve the target recognition performance in the target domain. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set illustrate that our framework is efficient. For the three-class recognition of the MSTAR data set, the recognition rate is about 87% even when only 23 samples of each class are utilized as the training data. For a given target data, the generation results with counterclockwise rotation of 1°–90° for the aspect angle are performed. The qualitative and quantitative comparisons between the generated images and the real data are also displayed.

SAR Target Recognition With Limited Training Data Based on Angular Rotation Generative Network

In this letter, the local scattering mechanism difference based on regression kernel (LSMDRK) is developed as a discriminative feature for ship detection. The LSMDRK measures the scattering mechanism dissimilarity of a center pixel to its neighboring pixels. A ship detection scheme is proposed based on the LSMDRK. The detection scheme consists of two stages. In the feature extraction stage, polarimetric target decomposition is required to improve the discriminative ability of the descriptor. In the detection stage, a saliency detection strategy is utilized to construct the saliency map. Then, local maximum detection is employed. Finally, an adaptive threshold method is designed to achieve the final detection. The effectiveness of the detection scheme is validated by a RADARSAT-2 data set. Experimental results demonstrate that the proposed method can acquire a better detection on weak targets and has much less false alarms than some classical detection methods.

PolSAR Ship Detection Using Local Scattering Mechanism Difference Based on Regression Kernel

The invention discloses a multiple-feature fusion SAR target identification method based on a convolutional neural network, and a problem of low SAR target discrimination performance in a complex scene in the prior art is mainly solved. The scheme comprises the steps of (1) preprocessing a given training set, and obtaining a new training set, (2) constructing an SAR target discrimination network based on a convolutional neural network, (3) inputting the new training set into the constructed SAR target discrimination network to carry out training, and obtaining a trained network, (4) preprocessing a given test set, and obtaining a new test set, and (5) inputting the new test set into the trained SAR target discrimination network, and obtaining a final target discrimination result. According to the method, the amplitude information and edge information of an SAR image are jointly utilized by the constructed SAR target discrimination network, the strong feature learning ability of the convolutional neural network is combined, the identification performance is improved, and the method can be used for the SAR target identification of a complex scene.

Multiple-feature fusion SAR target identification method based on convolutional neural network

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