  High-resolution aerial images have a wide range of applications, such as
military exploration, and urban planning. Semantic segmentation is a
fundamental method extensively used in the analysis of high-resolution aerial
images. However, the ground objects in high-resolution aerial images have the
characteristics of inconsistent scales, and this feature usually leads to
unexpected predictions. To tackle this issue, we propose a novel scale-aware
module (SAM). In SAM, we employ the re-sampling method aimed to make pixels
adjust their positions to fit the ground objects with different scales, and it
implicitly introduces spatial attention by employing a re-sampling map as the
weighted map. As a result, the network with the proposed module named
scale-aware network (SANet) has a stronger ability to distinguish the ground
objects with inconsistent scale. Other than this, our proposed modules can
easily embed in most of the existing network to improve their performance. We
evaluate our modules on the International Society for Photogrammetry and Remote
Sensing Vaihingen Dataset, and the experimental results and comprehensive
analysis demonstrate the effectiveness of our proposed module.
