Large-Scale Land Cover Mapping of Satellite Images Using Ensemble of Random Forests with Multi-Resolution Label - IEEE Data Fusion Contest 2020 Track 2

Huijun Chen, Changlin Xiao, Wei Liu, Rongjun Qin

Abstract: This paper describes our approach in the land cover classification with low- and high-resolution labels challenge of 2020 IEEE Data Fusion Contest. The challenge features the large-scale land cover mapping based on weakly annotated samples. We firstly refine the samples based on prior knowledge on class confusion and the confidence of the samples. Subsequently, an ensemble of random forests is used for classification and a post-processing step is performed to improve the results. This approach achieves an average accuracy of 0.6142 on the test dataset and achieves the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place in Track 2 of the Data Fusion Contest 2020.