The current deep learning studies on remote sensing mainly focused on deep learning algorithms rather than deep learning datasets. This study proposes a method of dataset consistency assessment based on deep learning, in which the similarity among various types of ships from different sources (such as satellite remote sensing, 3D modeling, and web crawler) is evaluated and then used to characterize the consistency of the ship dataset. The results show that when the consistency of the dataset is the highest, the consistency by the proposed method is 1. When the consistency of the datasets is gradient, the consistency also changes. Images with similar data sources can be considered as same class, and images with greatly differences cannot be merged. Thus, the proposed method can assess the dataset consistency properly, and provide a suggestion to build an image dataset for deep learning training. 

现有基于深度学习的遥感研究集中在算法开发方面, 缺乏对遥感样本库的研究。针对此问题，提出一种基于深度学习的样本库一致性评估方法, 对卫星遥感、三维建模和网络爬虫等不同来源的舰船样本库中各类样本之间的相似性进行评估。结果表明: 1) 在样本库一致性最高时, 该方法得到的一致性为1; 2) 当样本库一致性呈梯度变化时, 一致性评价结果随样本库一致性的变化而变化; 3) 数据来源相似的样本库能够进行合并, 制作方式差异较大的样本库不能合并。因此, 所提方法能够准确地评估样本库的一致性, 可为深度学习训练时样本库的选择提供参考。