综述深度学习在雷达船舶分类中的应用与挑战
A Survey on SAR ship classification using Deep Learning
- 构建首个基于深度学习模型、手工特征等的分类体系
- 指出数据稀缺、可解释性差是当前主要瓶颈
- 适合从事遥感图像分析与智能航运的研究者
深度学习已成为合成孔径雷达（SAR）船舶分类的强大工具。本综述全面分析了该领域中采用的多样化深度学习技术，识别出关键趋势与挑战，强调融合手工特征、使用公开数据集、数据增强、微调、可解释性技术以及跨学科合作对提升模型性能的重要性。本文建立了首个针对相关研究的分类体系，依据深度学习模型、手工特征使用、SAR属性利用及微调影响进行划分。讨论了船舶分类任务中所用方法及其影响，并探索未来研究方向，包括解决数据稀缺问题、探索新型深度学习架构、引入可解释性技术以及建立标准化评估指标。通过应对这些挑战并利用深度学习进展，研究人员可推动更准确高效的船舶分类系统发展，从而提升海事监视及相关应用水平。
原文摘要 · Abstract (English)
Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. This survey comprehensively analyzes the diverse DL techniques employed in this domain. We identify critical trends and challenges, highlighting the importance of integrating handcrafted features, utilizing public datasets, data augmentation, fine-tuning, explainability techniques, and fostering interdisciplinary collaborations to improve DL model performance. This survey establishes a first-of-its-kind taxonomy for categorizing relevant research based on DL models, handcrafted feature use, SAR attribute utilization, and the impact of fine-tuning. We discuss the methodologies used in SAR ship classification tasks and the impact of different techniques. Finally, the survey explores potential avenues for future research, including addressing data scarcity, exploring novel DL architectures, incorporating interpretability techniques, and establishing standardized performance metrics. By addressing these challenges and leveraging advancements in DL, researchers can contribute to developing more accurate and efficient ship classification systems, ultimately enhancing maritime surveillance and related applications.
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