Advances in high resolution remote sensing image analysis are currently hampered by the difficulty of gathering enough annotated data for training deep learning methods, giving rise to a variety of small datasets and associated dataset-specific methods. Moreover, typical tasks such as classification and retrieval lack a systematic evaluation on standard benchmarks and training datasets, which make it hard to identify durable and generalizable scientific contributions. We aim at unifying remote sensing image retrieval and classification with a new large-scale training and testing dataset, SF300, including both vertical and oblique aerial images and made available to the research community, and an associated fine-tuning method. We additionally propose a new adversarial fine-tuning method for global descriptors. We show that our framework systematically achieves a boost of retrieval and classification performance on nine different datasets compared to an ImageNet pretrained baseline, with currently no other method to compare to.

翻译：高分辨率遥感图像分析的进展目前因难以收集足够标注数据以训练深度学习方法而受阻，这导致了多种小型数据集及相关特定于数据集的方法的出现。此外，分类与检索等典型任务缺乏在标准基准和训练数据集上的系统评估，使得识别持久且可推广的科学贡献变得困难。我们旨在通过构建一个新的规模化训练与测试数据集SF300（包含垂直和倾斜航空图像，并向研究社区开放），以及一种关联的微调方法，来统一遥感图像检索与分类任务。我们还提出了一种用于全局描述符的新型对抗微调方法。实验表明，与基于ImageNet预训练的基线相比，我们的框架在九个不同数据集上系统地提升了检索与分类性能，且目前尚无其他方法可与之比较。