In this paper we present a method for building boundary re-finement and regularization in satellite images using a fullyconvolutional neural network trained with a combination ofadversarial and regularized losses. Compared to a pure MaskR-CNN model, the overall algorithm can achieve equivalentperformance in terms of accuracy and completeness. How-ever, unlike Mask R-CNN that produces irregular footprints,our framework generates regularized and visually pleasingbuilding boundaries which are beneficial in many applica-tions.
|Title of host publication||IGARSS 2019|
|Number of pages||4|
|Publication status||Published - 1 Jul 2019|
- Generative adversarial networks
- build-ing segmentation
- boundary refinement
- satellite images