Author:
Audebert Nicolas,Le Saux Bertrand,Lefèvre Sébastien
Funder
Total-ONERA research project NAOMI
Subject
Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics
Reference47 articles.
1. Audebert, N., Le Saux, B., Lefèvre, S., 2016. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks. In: Asian Conference on Computer Vision (ACCV16), Taipei, Taiwan, 2016.
2. Audebert, N., Le Saux, B., Lefèvre, S., 2016. How useful is region-based classification of remote sensing images in a deep learning framework? In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, pp. 5091–5094.
3. Audebert, N., Le Saux, B., Lefèvre, S., 2017. Joint learning from Earth observation and OpenStreetMap data to get faster better semantic maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, USA.
4. Badrinarayanan, V., Kendall, A., Cipolla, R., 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell..
5. Processing of extremely high-resolution LiDAR and RGB data: outcome of the 2015 IEEE GRSS data fusion contest Part A: 2-D contest;Campos-Taberner;IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens. PP,2016
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