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[RLF17] Deep Learning for Urban Remote Sensing
Conférence Internationale avec comité de lecture : Joint Urban Remote Sesing Event (JURSE 2017), March 2017, pp.-, Dubai,Mots clés: Deep Learning; Remote Sensing Images; Deformable Models
Résumé:
This work shows how deep learning techniques
can benefit to remote sensing. We focus on tasks which are
recurrent in Earth Observation data analysis. For classification
and semantic mapping of aerial images, we present various deep
network architectures and show that context information and
dense labeling allow to reach better performances. For estimation
of normals in point clouds, combining Hough transform with
convolutional networks also improves the accuracy of previous
frameworks by detecting hard configurations like corners. It
shows that deep learning allows to revisit remote sensing and
offers promising paths for urban modeling and monitoring.
Collaboration:
ONERA - DTIM
BibTeX
| @inproceedings { | |||
| RLF17, | |||
| title | = | "{Deep Learning for Urban Remote Sensing }", | |
| author | = | " H. Randrianarivo and B. Le Saux and M. Ferecatu ", | |
| booktitle | = | "{Joint Urban Remote Sesing Event (JURSE 2017)}", | |
| year | = | 2017, | |
| month | = | "March", | |
| pages | = | "-", | |
| address | = | " Dubai", | |
| } | |||
