  Label assignment is a crucial process in object detection, which
significantly influences the detection performance by determining positive or
negative samples during training process. However, existing label assignment
strategies barely consider the characteristics of targets in remote sensing
images (RSIs) thoroughly, e.g., large variations in scales and aspect ratios,
leading to insufficient and imbalanced sampling and introducing more
low-quality samples, thereby limiting detection performance. To solve the above
problems, an Elliptical Distribution aided Adaptive Rotation Label Assignment
(EARL) is proposed to select high-quality positive samples adaptively in
anchor-free detectors. Specifically, an adaptive scale sampling (ADS) strategy
is presented to select samples adaptively among multi-level feature maps
according to the scales of targets, which achieves sufficient sampling with
more balanced scale-level sample distribution. In addition, a dynamic
elliptical distribution aided sampling (DED) strategy is proposed to make the
sample distribution more flexible to fit the shapes and orientations of
targets, and filter out low-quality samples. Furthermore, a spatial distance
weighting (SDW) module is introduced to integrate the adaptive distance
weighting into loss function, which makes the detector more focused on the
high-quality samples. Extensive experiments on several popular datasets
demonstrate the effectiveness and superiority of our proposed EARL, where
without bells and whistles, it can be easily applied to different detectors and
achieve state-of-the-art performance. The source code will be available at:
https://github.com/Justlovesmile/EARL.
