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Satellite imagery-based ship detection is indispensable in maritime surveillance and monitoring the naval activities. Machine learning is an effective approach that enables the process to be automatic and more accurate as compared to many other approaches. Generally, optical and synthetic aperture radar satellite images are often employed for detecting/locating various marine activities using different methods. However, models trained on one set of images often yield large uncertainties when testing on other sets of images due to the complex scene characteristics. This study proposes a novel lightweight computationally efficient deep learning-based general ship detection model called the Multi- Attentive General Ship Detector (MAGSD) for detecting ships in both optical and SAR satellite images. The model is trained with the SAR Ship Dataset (SDD), which has ship instances from Gaofen-3 and Sentinel-1 SAR satellite images, and the MASATI dataset that contains ship instances from the Microsoft Bing Map. The proposed model focuses on the attention-guided convolutional neural network for extracting feature maps for detection, which bridges the gap between SAR and optical image characteristics constraints by focusing on different levels of convolutional features in the network. The model is built with a novel feature extractor that has fourteen convolutional layers with six max pool layers and six attention layers, connecting several convolutional points to focus on local features in different depth maps which serve as the backbone of the model. The comparative analysis showed the robustness of the proposed model over the state-of-the-art baseline model YOLOv5s, with a precision of 8.2% and a recall of 9.63%. These results indicate that the proposed model holds the potential to serve as an efficient tool for ship detection in any satellite images and contributes to the enhanced coastal surveillance and bolsters global naval security.
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