This study proposes a novel transfer learning framework for effective ship classification using high-resolution optical remote sensing satellite imagery. The framework is based on the deep convolutional neural network model ResNet50 and incorporates the Convolutional Block Attention Module (CBAM) to enhance performance. CBAM enables the model to attend to salient features in the images, allowing it to better discriminate between subtle differences between ships and backgrounds. Furthermore, this study adopts a transfer learning approach tailored for accurately classifying diverse types of ships by fine-tuning a pre-trained model for the specific task. Experimental results demonstrate the efficacy of the proposed framework in ship classification using optical remote sensing imagery, achieving a high classification accuracy of 94% across 5 classes, outperforming existing methods. This research holds potential applications in maritime surveillance and management, illegal fishing detection, and maritime traffic monitoring.

使用高分辨率光学遥感卫星图像，本研究提出了一种新颖的传输学习框架，用于有效的船舶分类。该框架基于深度卷积神经网络模型 ResNet50，并结合了卷积块注意力模块 (CBAM) 来增强性能。实验结果表明该框架在光学遥感图像的船舶分类中具有良好的效果，达到了94%的高分类精度，超过了现有方法。该研究在海上监视与管理、非法渔业监测和海上交通监控方面具有潜在的应用价值。

用光学卫星影像的迁移学习进行船只分类的ResNet集成卷积块注意模块