D. Cerra, N. Merkle, C. Henry, K. Alonso, P. d’Angelo, S. Auer, R. Bahmanyar, X. Yuan, K. Bittner, M. Langheinrich, Guichen Zhang, M. Pato, Jiaojiao Tian, P. Reinartz
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Stepwise Refinement Of Low Resolution Labels For Earth Observation Data: Part 2
This paper describes the contribution of the DLR team ranking 2nd in Track 2 of the 2020 IEEE GRSS Data Fusion Contest. The semantic classification of multimodal earth observation data proposed is based on the refinement of low-resolution MODIS labels, using as auxiliary training data higher resolution labels available for a validation data set. The classification is initialized with a handcrafted decision tree integrating output from a random forest classifier, and subsequently boosted by detectors for specific classes. The results of the team ranking 3rd in Track 1 of the same contest are reported in a companion paper.
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