Annotating remote sensing images (RSIs) presents a notable challenge due to its labor-intensive nature. Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data, assuming that all classes found in the unlabeled dataset are also represented in the labeled data. However, real-world situations introduce the possibility of out-of-distribution (OOD) samples being mixed with in-distribution (ID) samples within the unlabeled dataset. In this paper, we delve into techniques for conducting SSOD directly on uncurated unlabeled data, which is termed Open-Set Semi-Supervised Object Detection (OSSOD). Our approach commences by employing labeled in-distribution data to dynamically construct a class-wise feature bank (CFB) that captures features specific to each class. Subsequently, we compare the features of predicted object bounding boxes with the corresponding entries in the CFB to calculate OOD scores. We design an adaptive threshold based on the statistical properties of the CFB, allowing us to filter out OOD samples effectively. The effectiveness of our proposed method is substantiated through extensive experiments on two widely used remote sensing object detection datasets: DIOR and DOTA. These experiments showcase the superior performance and efficacy of our approach for OSSOD on RSIs.

翻译：遥感图像的标注因劳动密集型特性而面临显著挑战。半监督目标检测方法通过为未标注数据生成伪标签来解决这一问题，其前提是未标注数据集中包含的所有类别均在已标注数据中有所体现。然而，现实场景中未标注数据集可能混杂分布外样本与分布内样本。本文探讨了直接对非精选未标注数据进行半监督目标检测的技术，即开放集半监督目标检测。我们的方法首先利用已标注的分布内数据动态构建类别特征库，以捕捉每个类别的特定特征；随后，将预测的目标边界框特征与类别特征库中的对应条目进行比对，计算分布外得分。基于类别特征库的统计特性，我们设计了一种自适应阈值，可有效滤除分布外样本。通过在DIOR和DOTA这两个广泛使用的遥感目标检测数据集上开展的大量实验，验证了所提方法在遥感图像开放集半监督目标检测中的优越性能与有效性。