Abstract
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.
Abstract (translated)
这项研究提出了一种新颖的迁移学习框架,用于使用高分辨率光学遥感卫星图像有效地对船只进行分类。该框架基于深度卷积神经网络模型ResNet50,并采用了卷积块注意模块(CBAM)来提高性能。CBAM使模型能够关注图像中的显着特征,从而更好地区分船只和背景之间的微妙差异。此外,本研究采用了一种针对准确分类各种船只的迁移学习方法,通过微调预训练模型来适应特定任务。实验结果表明,所提出的框架在光学遥感图像中进行船只分类的有效性,达到5个类别的高分类准确率94%,超过了现有方法。这项研究具有在海上监视和管理、非法捕鱼检测和海上交通监测等领域潜在应用的价值。
URL
https://arxiv.org/abs/2404.02135
