Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with the ImageNet pretrained weights. Since natural images inevitably present a large domain gap relative to aerial images, probably limiting the finetuning performance on downstream aerial scene tasks. This issue motivates us to conduct an empirical study of remote sensing pretraining (RSP) on aerial images. To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks. Then, we investigate the impact of RSP on representative downstream tasks including scene recognition, semantic segmentation, object detection, and change detection using these CNN and vision transformer backbones. Empirical study shows that RSP can help deliver distinctive performances in scene recognition tasks and in perceiving RS related semantics such as "Bridge" and "Airplane". We also find that, although RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, it may still suffer from task discrepancies, where downstream tasks require different representations from scene recognition tasks. These findings call for further research efforts on both large-scale pretraining datasets and effective pretraining methods. The codes and pretrained models will be released at https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing.

翻译：深度学习极大重塑了遥感（RS）领域对航拍图像理解的研究，并取得了巨大成功。然而，现有深度学习模型大多采用ImageNet预训练权重初始化。由于自然图像与航拍图像之间不可避免地存在领域差异，这可能限制下游航拍场景任务的微调性能。这一问题促使我们对航拍图像的遥感预训练（RSP）开展实证研究。为此，我们借助迄今为止最大的遥感场景识别数据集——MillionAID，从零开始训练不同网络结构，获得一系列遥感预训练骨干模型，包括卷积神经网络（CNN）以及视觉Transformer（如Swin和ViTAE），这些模型已在计算机视觉任务中展现出色性能。随后，我们使用这些CNN和视觉Transformer骨干网络，考察RSP对场景识别、语义分割、目标检测和变化检测等代表性下游任务的影响。实证研究表明，RSP能在场景识别任务及感知“桥梁”、“飞机”等遥感相关语义方面带来显著性能提升。我们还发现，尽管RSP缓解了传统ImageNet预训练在遥感图像上的数据分布差异，但仍可能面临任务差异问题——即下游任务需要与场景识别任务不同的表征。这些发现呼吁进一步研究大规模预训练数据集和高效预训练方法。代码与预训练模型将发布在https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing。