论文标题
空间之外的颜色：学习地球观察图像的自我监督表示形式
The color out of space: learning self-supervised representations for Earth Observation imagery
论文作者
论文摘要
卫星图像数量最近的增长促进了遥感（RS）的有效深度学习技术的发展。但是，由于缺乏大量注释的数据集，它们的全部潜力未开发。通常，通过微调以前在Imagenet数据集上训练的功能提取器来应对此问题。不幸的是，自然图像的领域与RS域不同，这阻碍了最终性能。在这项工作中，我们建议从卫星图像中学习有意义的表示形式，以利用其高维谱带来重建可见的颜色。我们对土地覆盖分类（Bigearthnet）和西尼罗河病毒检测进行实验，表明着色是训练特征提取器的坚实借口。此外，我们定性地观察到，基于自然图像和着色的猜测依赖于输入的不同部分。这为整体模型铺平了道路，该模型最终优于上述技术。
The recent growth in the number of satellite images fosters the development of effective deep-learning techniques for Remote Sensing (RS). However, their full potential is untapped due to the lack of large annotated datasets. Such a problem is usually countered by fine-tuning a feature extractor that is previously trained on the ImageNet dataset. Unfortunately, the domain of natural images differs from the RS one, which hinders the final performance. In this work, we propose to learn meaningful representations from satellite imagery, leveraging its high-dimensionality spectral bands to reconstruct the visible colors. We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor. Furthermore, we qualitatively observe that guesses based on natural images and colorization rely on different parts of the input. This paves the way to an ensemble model that eventually outperforms both the above-mentioned techniques.
