Stepwise Refinement Of Low Resolution Labels For Earth Observation Data: Part 2

This paper describes the contribution of the DLR team ranking 2
^nd
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 3
^rd
in Track 1 of the same contest are reported in a companion paper.