Semantic segmentation requires pixel-level annotation, which is
time-consuming. Active Learning (AL) is a promising method for reducing data
annotation costs. Due to the gap between aerial and natural images, the
previous AL methods are not ideal, mainly caused by unreasonable labeling units
and the neglect of class imbalance. Previous labeling units are based on images
or regions, which does not consider the characteristics of segmentation tasks
and aerial images, i.e., the segmentation network often makes mistakes in the
edge region, and the edge of aerial images is often interlaced and irregular.
Therefore, an edge-guided labeling unit is proposed and supplemented as the new
unit. On the other hand, the class imbalance is severe, manifested in two
aspects: the aerial image is seriously imbalanced, and the AL strategy does not
fully consider the class balance. Both seriously affect the performance of AL
in aerial images. We comprehensively ensure class balance from all steps that
may occur imbalance, including initial labeled data, subsequent labeled data,
and pseudo-labels. Through the two improvements, our method achieves more than
11.2\% gains compared to state-of-the-art methods on three benchmark datasets,
Deepglobe, Potsdam, and Vaihingen, and more than 18.6\% gains compared to the
baseline. Sufficient ablation studies show that every module is indispensable.
Furthermore, we establish a fair and strong benchmark for future research on AL
for aerial image segmentation.

提出了基于边缘指导的标注单元作为新单元以及对类别不平衡的综合性处理，改进的方法在三个基准数据集上相比最先进方法获得了超过 11.2% 的性能提升，并相比基准模型提高了超过 18.6% 的性能表现，为未来关于航空图像语义分割的主动学习研究建立了公平而强大的基准。