  Assigning geospatial objects with specific categories at the pixel level is a
fundamental task in remote sensing image analysis. Along with rapid development
in sensor technologies, remotely sensed images can be captured at multiple
spatial resolutions (MSR) with information content manifested at different
scales. Extracting information from these MSR images represents huge
opportunities for enhanced feature representation and characterisation.
However, MSR images suffer from two critical issues: 1) increased scale
variation of geo-objects and 2) loss of detailed information at coarse spatial
resolutions. To bridge these gaps, in this paper, we propose a novel
scale-aware neural network (SaNet) for semantic segmentation of MSR remotely
sensed imagery. SaNet deploys a densely connected feature network (DCFFM)
module to capture high-quality multi-scale context, such that the scale
variation is handled properly and the quality of segmentation is increased for
both large and small objects. A spatial feature recalibration (SFRM) module is
further incorporated into the network to learn intact semantic content with
enhanced spatial relationships, where the negative effects of information loss
are removed. The combination of DCFFM and SFRM allows SaNet to learn
scale-aware feature representation, which outperforms the existing multi-scale
feature representation. Extensive experiments on three semantic segmentation
datasets demonstrated the effectiveness of the proposed SaNet in
cross-resolution segmentation.
