Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

Intelligent ship detection based on high-precision synthetic aperture radar (SAR) images plays a vital role in ocean monitoring and maritime management. Denoising is an effective preprocessing step for target detection. Morphological network-based denoising can effectively remove speckle noise, while the smoothing effect of which blurs the edges of the image and reduces the detection accuracy. The fusion of edge extraction and morphological network can improve detection accuracy by compensating for the lack of edge information caused by smoothing. This article proposes an end-to-end lightweight network called morphological feature-pyramid Yolo v4-tiny for SAR ship detection. First, a morphological network is introduced to preprocess the SAR images for speckle noise suppression and edge enhancement, providing spatial high-frequency information for target detection. Then, the original and preprocessed images are combined into the multichannel as an input for the convolution layer of the network. The feature pyramid fusion structure is used to extract the high-level semantic features and shallow detailed features from the image, improving the performance of multiscale target detection. Experiments on the public SAR ship detection dataset and AIR SARShip-1.0 show that the proposed method performs better than the other convolution neural network-based methods.

In order to more efficiently mine the features of PolSAR images and build a more suitable classification model that combines the features of the polarimetric domain and the spatial domain, this paper proposes a PolSAR image classification method, called low-frequency and contour sub-bands driven polarimetric Squeeze-and-Excitation network (LC-PSENet). First, the proposed LC-PSENet introduces the nonsubsampled Laplacian pyramid to decompose polarimetric feature maps, so as to construct a multi-channel PolSAR image based on the low-frequency sub-band and contour sub-band of these maps. It guides the network to perform feature mining and selection in the sub-bands of each polarimetric map in a supervised way, automatically balancing the contributions of polarimetric features and their sub-bands and the influence of interference information such as noise, making the network learning more efficient. Second, the method introduces Squeeze-and-Excitation operation in CNN to perform channel modeling on the polarimetric feature sub-bands. It strengthens the learning of the contributions of local maps of the polarimetric features and sub-bands, thereby effectively combining the features of the polarimetric domain and the spatial domain. Experiments on the datasets of Flevoland, Netherlands and Oberpfaffenhofen show that the proposed LC-PSENet achieves overall accuracies of 99.66%, 99.72%, and 95.89%, which are 0.87%, 0.27% and 1.42% higher than the baseline CNN, respectively. The isolated points in the classification results are obviously reduced, and the distinction between boundary and non-boundary is more clear and delicate. Also, the method performs better than many current state-of-the-art methods in terms of classification accuracy.

Speckle noise is an important factor affecting the accuracy of synthetic aperture radar (SAR) target recognition. Traditional speckle reduction methods based on transform domain and spatial filtering usually require professional experience to set the threshold, which will also affect the recognition accuracy. This letter proposes a multilevel wavelet speckle reduction network (Wavelet-SRNet) for noisy SAR images target recognition. First, the method designs the wavelet soft threshold denoising method as a trainable neural network module in the convolutional neural network (CNN) framework. Then, a two-level wavelet denoising branch is constructed and fused with the original noisy image. Finally, we cascade a CNN-based classification model on the above structure to form an SAR image target recognition network whose denoising threshold can be automatically learned. Experiments on the moving and stationary target acquisition and recognition database show that the classification accuracy of the proposed method for target recognition in noisy SAR images is better than the compared state-of-the-art methods. Also, the method achieved high test accuracy in the noise augmentation experiment.

Unmanned aerial vehicles (UAVs) open up new opportunities for transportation monitoring. However, the vehicle targets in UAV images are situated in dynamic scenarios, such as uncertain background, dramatically varying arrangement density, multi-scale, and arbitrary-oriented. Most strategies for UAV-based monitoring require complex manoeuvring and still lack accurate abilities and lightweight structures. Consequently, designing effective detection methods with both speed and accuracy is challenging. This paper proposes a lightweight YOLO-based arbitrary-oriented vehicle detector via precise positional information encoding and bidirectional feature fusion to address the above issues. First, an additional angular classification prediction branch is added to the YOLO head network to significantly improve the detection performance for arbitrary-oriented vehicles without incurring the extra computational complexity and burden. Second, a C3 module embedded coordinate attention (C3CA) is presented to capture long-range dependencies and preserve vehicles’ precise positional information in feature maps. Then, a fully connected bidirectional feature fusion module (FC-BiFPN) is applied at the neck of the YOLO detection framework, which is helpful for multi-scale vehicle detection. This module can efficiently aggregate features at different resolutions and automatically enhance information interaction. Finally, experiments and comparisons on vehicle and remote sensing datasets demonstrate that our approach outperforms the state-of-the-art methods in balancing precision and efficiency. In addition, the overall network design follows the lightweight concept, which better meets the real-time requirements of the UAV urban traffic monitoring platform in realistic scenarios.

Radar radio source (RRS) recognition plays an important role in the fields of military electronic support systems (ESM) and civilian autonomous driving. The rapid development of machine learning technology, especially deep learning, has effectively and efficiently improved RRS intelligent recognition performances when operating in the increasingly complex electromagnetic environment. However, the data sampling limitation and computation cost are still severe challenges in real RRS recognition scenarios. In this paper, we propose a novel network based on meta-transfer learning, called RRSARNet, to achieve effective adaptive RRS recognition in the context of low signal-to-noise ratio (SNR). First, by using the short-time Fourier transform, a six-type small samples RRS simulation dataset with different SNR levels is constructed. Then, a novel RRSARNet, based on metric learning, is proposed, which consists of a four-layer embedding module and a four-layer relational module. Finally, the RRS dataset is divided into training, supporting and testing subsets, which are used to train and test the RRSARNet in a meta-transfer learning method. Experiments on the RRS dataset show that the proposed RRSARNet can achieve an overall accuracy (OA) above 96% and 99% when the SNR is above −15 dB and −10 dB, respectively. Even when the SNR is -30 dB, OA can reach more than 70%. For 5-way 1-shot and 5-way 5-shot experiments, the inference time of an image is about 0.043 and 0.140 milliseconds, respectively. Besides, experiments on the RRS simulation dataset and the two benchmark datasets, the RRSARNet performs better or more competitive than many existing state-of-the-art technologies in terms of recognition accuracy.