  Semantic segmentation of Very High Resolution (VHR) remote sensing images is
a fundamental task for many applications. However, large variations in the
scales of objects in those VHR images pose a challenge for performing accurate
semantic segmentation. Existing semantic segmentation networks are able to
analyse an input image at up to four resizing scales, but this may be
insufficient given the diversity of object scales. Therefore, Multi Scale (MS)
test-time data augmentation is often used in practice to obtain more accurate
segmentation results, which makes equal use of the segmentation results
obtained at the different resizing scales. However, it was found in this study
that different classes of objects had their preferred resizing scale for more
accurate semantic segmentation. Based on this behaviour, a Stacking-Based
Semantic Segmentation (SBSS) framework is proposed to improve the segmentation
results by learning this behaviour, which contains a learnable Error Correction
Module (ECM) for segmentation result fusion and an Error Correction Scheme
(ECS) for computational complexity control. Two ECS, i.e., ECS-MS and ECS-SS,
are proposed and investigated in this study. The Floating-point operations
(Flops) required for ECS-MS and ECS-SS are similar to the commonly used MS test
and the Single-Scale (SS) test, respectively. Extensive experiments on four
datasets (i.e., Cityscapes, UAVid, LoveDA and Potsdam) show that SBSS is an
effective and flexible framework. It achieved higher accuracy than MS when
using ECS-MS, and similar accuracy as SS with a quarter of the memory footprint
when using ECS-SS.
