DeNISE: Deep Networks for Improved Segmentation Edges
Sander Riisøen Jyhne Per-Arne Andersen Morten Goodwin
This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a
novel data enhancement technique using edge detection and segmentation models
to improve the boundary quality of segmentation masks. DeNISE utilizes the
inherent differences in two sequential deep neural architectures to improve the
accuracy of the predicted segmentation edge. DeNISE applies to all types of
neural networks and is not trained end-to-end, allowing rapid experiments to
discover which models complement each other. We test and apply DeNISE for
building segmentation in aerial images. Aerial images are known for difficult
conditions as they have a low resolution with optical noise, such as
reflections, shadows, and visual obstructions. Overall the paper demonstrates
the potential for DeNISE. Using the technique, we improve the baseline results
with a building IoU of 78.9%.