3D Semantic Segmentation from Multi-View Optical Satellite Images
Pablo d’Angelo, Ksenia Bittner, Thomas F. Krauss, Peter H. Reinartz, Daniele Cerra, Seyed Majid Azimi, Nina Merkle, Jiaojiao Tian, Stefan Auer, Miguel Pato, Raquel de los Reyes, Xiangyu Zhuo · 2019
This paper describes the winning contribution to the 2019 IEEE GRSS Data Fusion Contest Multi-view Semantic Stereo Challenge. In this challenge, a digital surface model (DSM) and a semantic segmentation should be derived from a large number of multi-spectral WorldView-3 images. Results from 50 stereo pairs matched using Semi-Global Matching (SGM) are fused into a DSM. Semantic segmentation is performed with an ensemble of FCN networks taking as input RGB, multi-spectral and height data. Their results are then merged with pixel-wise detectors for the classes water and high vegetation. Compared to the second and third placed teams (mIOU-3 scores of 0.73 and 0.7295), our contribution reached a significantly higher score of 0.745.