With the continuous advancement of science and technology, there has been a growing awareness of safety among people worldwide. Natural disasters such as wildfires, earthquakes, and floods pose persistent threats to both lives and property on our planet, which serves as our fundamental habitat. While it is impossible to prevent or entirely avert these calamities, rapid identification of affected areas and prompt damage assessment post-disaster can significantly aid in the formulation of effective rescue strategies, ultimately saving more lives. This article delves into the application of transfer learning in satellite image damage assessment—a methodology that involves transferring previously acquired knowledge to enhance a model's adaptability to new tasks. Given the limited availability of datasets for satellite image analysis, transfer learning proves to be an effective approach. Specifically, the study proposes a transfer learning method based on YOLOv5 for satellite image damage assessment. Initially, a general convolutional neural network model is trained using a substantial dataset of natural images. Subsequently, the early layers of this model are frozen, while the later layers undergo training to adapt to satellite image data. Fine-tuning is then employed to further enhance the overall model performance. The results demonstrate that this approach yields a high accuracy rate in satellite image damage assessment. Moreover, compared to conventional deep learning methods, the proposed method effectively leverages pre-trained models' knowledge, thereby reducing data dependency. Additionally, it displays robust generalization capabilities across diverse tasks and datasets, underscoring its potential for facilitating transfer learning across various domains.

中文翻译：

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用于农业损害和变化检测的遥感增强迁移学习方法：深度学习视角

随着科学技术的不断进步，全世界人们的安全意识不断增强。野火、地震和洪水等自然灾害对作为我们基本栖息地的地球上的生命和财产构成持续威胁。虽然不可能预防或完全避免这些灾难，但快速识别受影响地区并在灾后及时进行损失评估可以极大地帮助制定有效的救援策略，最终挽救更多生命。本文深入探讨了迁移学习在卫星图像损伤评估中的应用——一种涉及迁移先前获得的知识以增强模型对新任务的适应性的方法。鉴于卫星图像分析数据集的可用性有限，迁移学习被证明是一种有效的方法。具体来说，该研究提出了一种基于YOLOv5的用于卫星图像损伤评估的迁移学习方法。最初，使用大量自然图像数据集来训练通用卷积神经网络模型。随后，该模型的早期层被冻结，而后面的层则接受训练以适应卫星图像数据。然后进行微调以进一步提高整体模型性能。结果表明，该方法在卫星图像损伤评估中具有较高的准确率。此外，与传统的深度学习方法相比，该方法有效地利用了预训练模型的知识，从而减少了数据依赖性。此外，它还显示出跨不同任务和数据集的强大泛化能力，强调了其促进跨不同领域迁移学习的潜力。