Rotated object detection has made significant progress in the optical remote sensing. However, advancements in the Synthetic Aperture Radar (SAR) field are laggard behind, primarily due to the absence of a large-scale dataset. Annotating such a dataset is inefficient and costly. A promising solution is to employ a weakly supervised model (e.g., trained with available horizontal boxes only) to generate pseudo-rotated boxes for reference before manual calibration. Unfortunately, the existing weakly supervised models exhibit limited accuracy in predicting the object's angle. Previous works attempt to enhance angle prediction by using angle resolvers that decouple angles into cosine and sine encodings. In this work, we first reevaluate these resolvers from a unified perspective of dimension mapping and expose that they share the same shortcomings: these methods overlook the unit cycle constraint inherent in these encodings, easily leading to prediction biases. To address this issue, we propose the Unit Cycle Resolver, which incorporates a unit circle constraint loss to improve angle prediction accuracy. Our approach can effectively improve the performance of existing state-of-the-art weakly supervised methods and even surpasses fully supervised models on existing optical benchmarks (i.e., DOTA-v1.0 dataset). With the aid of UCR, we further annotate and introduce RSAR, the largest multi-class rotated SAR object detection dataset to date. Extensive experiments on both RSAR and optical datasets demonstrate that our UCR enhances angle prediction accuracy. Our dataset and code can be found at: https://github.com/zhasion/RSAR.

翻译：旋转目标检测在光学遥感领域已取得显著进展。然而，合成孔径雷达（SAR）领域的进展相对滞后，这主要归因于缺乏大规模数据集。标注此类数据集效率低下且成本高昂。一种可行的解决方案是采用弱监督模型（例如仅使用可用水平框进行训练）在人工校准前生成参考用的伪旋转框。遗憾的是，现有弱监督模型在预测目标角度方面精度有限。先前研究尝试通过使用角度解析器（将角度解耦为余弦和正弦编码）来提升角度预测性能。本文首先从维度映射的统一视角重新评估这些解析器，揭示它们存在共同缺陷：这些方法忽略了此类编码固有的单位圆约束，易导致预测偏差。为解决该问题，我们提出单位圆约束解析器，通过引入单位圆约束损失函数提升角度预测精度。该方法能有效提升现有先进弱监督模型的性能，甚至在现有光学基准数据集（即DOTA-v1.0数据集）上超越全监督模型。借助UCR，我们进一步标注并发布了RSAR——迄今为止规模最大的多类别旋转SAR目标检测数据集。在RSAR和光学数据集上的大量实验表明，我们的UCR显著提升了角度预测精度。数据集与代码可通过以下链接获取：https://github.com/zhasion/RSAR。