Pointly Supervised Object Detection (PSOD) has attracted considerable interests due to its lower labeling cost as compared to box-level supervised object detection. However, the complex scenes, densely packed and dynamic-scale objects in Remote Sensing (RS) images hinder the development of PSOD methods in RS field. In this paper, we make the first attempt to achieve RS object detection with single point supervision, and propose a PSOD framework tailored with RS images. Specifically, we design a point label upgrader (PLUG) to generate pseudo box labels from single point labels, and then use the pseudo boxes to supervise the optimization of existing detectors. Moreover, to handle the challenge of the densely packed objects in RS images, we propose a sparse feature guided semantic prediction module which can generate high-quality semantic maps by fully exploiting informative cues from sparse objects. Extensive ablation studies on the DOTA dataset have validated the effectiveness of our method. Our method can achieve significantly better performance as compared to state-of-the-art image-level and point-level supervised detection methods, and reduce the performance gap between PSOD and box-level supervised object detection. Code will be available at https://github.com/heshitian/PLUG.

翻译：点监督目标检测（PSOD）因其相较于框级监督目标检测更低的标注成本而受到广泛关注。然而，遥感图像中场景复杂、目标密集且尺度动态变化的特性阻碍了PSOD方法在该领域的发展。本文首次尝试实现基于单点监督的遥感目标检测，并提出一种专为遥感图像设计的PSOD框架。具体而言，我们设计了一个点标签升级器（PLUG），用于从单点标签生成伪框标签，并利用这些伪框监督现有检测器的优化过程。此外，为应对遥感图像中目标密集排列的挑战，我们提出了一种稀疏特征引导的语义预测模块，该模块通过充分挖掘稀疏目标中的信息线索来生成高质量语义图。在DOTA数据集上的大量消融实验验证了所提方法的有效性。与当前最先进的图像级和点级监督检测方法相比，我们的方法能够显著提升性能，并缩小PSOD与框级监督目标检测之间的性能差距。代码将在 https://github.com/heshitian/PLUG 公开。