In recent years Convolutional neural networks (CNN) have made significant progress in computer vision. These advancements have been applied to other areas, such as remote sensing and have shown satisfactory results. However, the lack of large labeled datasets and the inherent complexity of remote sensing problems have made it difficult to train deep CNNs for dense prediction problems. To solve this issue, ImageNet pretrained weights have been used as a starting point in various dense predictions tasks. Although this type of transfer learning has led to improvements, the domain difference between natural and remote sensing images has also limited the performance of deep CNNs. On the other hand, self-supervised learning methods for learning visual representations from large unlabeled images have grown substantially over the past two years. Accordingly, in this paper we have explored the effectiveness of in-domain representations in both supervised and self-supervised forms to solve the domain difference between remote sensing and the ImageNet dataset. The obtained weights from remote sensing images are utilized as initial weights for solving semantic segmentation and object detection tasks and state-of-the-art results are obtained. For self-supervised pre-training, we have utilized the SimSiam algorithm as it is simple and does not need huge computational resources. One of the most influential factors in acquiring general visual representations from remote sensing images is the pre-training dataset. To examine the effect of the pre-training dataset, equal-sized remote sensing datasets are used for pre-training. Our results have demonstrated that using datasets with a high spatial resolution for self-supervised representation learning leads to high performance in downstream tasks.

翻译：近年来，卷积神经网络（CNN）在计算机视觉领域取得了显著进步。这些进展已被应用于遥感等其他领域，并展现了令人满意的成果。然而，大型标注数据集的缺乏以及遥感问题固有的复杂性，使得训练深度CNN解决密集预测问题变得困难。为解决这一问题，ImageNet预训练权重被用作各种密集预测任务的起点。尽管这种迁移学习带来了改进，但自然图像与遥感图像之间的域差异限制了深度CNN的性能。另一方面，过去两年中，从大规模无标签图像中学习视觉表示的自监督学习方法大幅增长。因此，本文探索了有监督和自监督形式的域内表示在解决遥感与ImageNet数据集之间域差异方面的有效性。使用从遥感图像获得的权重作为解决语义分割和目标检测任务的初始权重，并取得了最先进的成果。对于自监督预训练，我们采用了SimSiam算法，因其简单且无需大量计算资源。获取遥感图像通用视觉表示的最关键因素之一是预训练数据集。为检验预训练数据集的影响，我们使用等规模的遥感数据集进行预训练。结果表明，使用高空间分辨率的数据集进行自监督表示学习，在下游任务中能获得高性能。