To better understand scene images in the field of remote sensing, multi-label annotation of scene images is necessary. Moreover, to enhance the performance of deep learning models for dealing with semantic scene understanding tasks, it is vital to train them on large-scale annotated data. However, most existing datasets are annotated by a single label, which cannot describe the complex remote sensing images well because scene images might have multiple land cover classes. Few multi-label high spatial resolution remote sensing datasets have been developed to train deep learning models for multi-label based tasks, such as scene classification and image retrieval. To address this issue, in this paper, we construct a multi-label high spatial resolution remote sensing dataset named MLRSNet for semantic scene understanding with deep learning from the overhead perspective. It is composed of high-resolution optical satellite or aerial images. MLRSNet contains a total of 109,161 samples within 46 scene categories, and each image has at least one of 60 predefined labels. We have designed visual recognition tasks, including multi-label based image classification and image retrieval, in which a wide variety of deep learning approaches are evaluated with MLRSNet. The experimental results demonstrate that MLRSNet is a significant benchmark for future research, and it complements the current widely used datasets such as ImageNet, which fills gaps in multi-label image research. Furthermore, we will continue to expand the MLRSNet. MLRSNet and all related materials have been made publicly available at https://data.mendeley.com/datasets/7j9bv9vwsx/1 and https://github.com/cugbrs/MLRSNet.git.

为了更好地理解遥感领域中的场景图像，需要对场景图像进行多标签注释。此外，要提高深度学习模型处理语义场景理解任务的性能，对它们进行大规模带注释数据的训练至关重要。但是，大多数现有数据集都由单个标签标注，由于场景图像可能具有多个土地覆被类别，因此无法很好地描述复杂的遥感图像。很少有多标签高空间分辨率的遥感数据集已经开发出来，可以为基于多标签的任务（例如场景分类和图像检索）训练深度学习模型。为了解决这个问题，在本文中，我们构建了一个名为MLRSNet的多标签高分辨率遥感数据集，用于从开销的角度进行深度学习的语义场景理解。它由高分辨率光学卫星或航空影像组成。MLRSNet包含46个场景类别中的109,161个样本，每个图像至少具有60个预定义标签之一。我们设计了视觉识别任务，包括基于多标签的图像分类和图像检索，其中使用MLRSNet评估了多种深度学习方法。实验结果表明，MLRSNet是未来研究的重要基准，它补充了ImageNet等当前广泛使用的数据集，填补了多标签图像研究的空白。此外，我们将继续扩展MLRSNet。