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.

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In order to more efficiently mine the features of Pol-SAR images and build a more suitable classification model that combines the features of the polarimetric domain and the spatial domain, this article proposes a PolSAR image classification method, called low-frequency and contour subbands-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 multichannel PolSAR image based on the low-frequency subband and contour subband of these maps. It guides the network to perform feature mining and selection in the subbands of each polarimetric map in a supervised way, automatically balancing the contributions of polarimetric features and their subbands and the influence of interference information such as noise, making the network learning more efficient. Second, the method introduces squeeze-andexcitation operation in the convolutional neural network (CNN) to perform channel modeling on the polarimetric feature subbands. It strengthens the learning of the contributions of local maps of the polarimetric features and subbands, thereby, effectively combining the features of the polarimetric domain and the spatial domain. Experiments on the datasets of Flevoland, The 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 nonboundary is more clear and delicate. Also, the method performs better than many current state-of-the-art methods in terms of classification accuracy.