Multimodal, multitemporal, and multisource global data fusion for local climate zones classification based on ensemble learning

Abstract

This paper presents a new methodology for classification of local climate zones based on ensemble learning techniques. Landsat-8 data and open street map data are used to extract spectral-spatial features, including spectral reflectance, spectral indexes, and morphological profiles fed to subsequent classification methods as inputs. Canonical correlation forests and rotation forests are used for the classification step. The final classification map is generated by majority voting on different classification maps obtained by the two classifiers using multiple training subsets. The proposed method achieved an overall accuracy of 74.94% and a kappa coefficient of 0.71 in the 2017 IEEE GRSS Data Fusion Contest.