Semantic Segmentation Refinement with Deep Edge Superpixels to Enhance Historical Land Cover
Résumé
In this work, we explore a post-processing method to enhance coarse semantic segmentation of historical aerial images. We propose to use deep edges to generate semantically meaningful superpixels that we integrate as additional pairwise potentials in a dense conditional random field. We apply our approach on very high resolution images acquired between 1975 and 1995 and annotated with land use land cover labels. Results show the interest of our approach compared to other post-processing methods.