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Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones by Zhang, Guichen; Ghamisi, Pedram; Zhu, Xiao Xiang is a scholarly article available to read on EtoBox.

What is Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones about?

This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). Feature stacking is the most commonly-used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM data, which becomes its main drawback. The proposed framework processes two data sources separately and then combines them at the model level through two fusion models (the landuse fusion model and building fusion model), which aim to fuse optical images with landuse and buildings layers of OSM data, respectively. In addition, a new approach to detecting building incompleteness of OSM data is proposed. The proposed framework was trained and tested using data from the 2017 IEEE GRSS Data Fusion Contest, and further validated on one additional test set containing test samples which are manually labeled in Munich and New York. Experimental results have indicated that compared to the feature stacking-based baseline framework the proposed framework is effective in fusing optical images with OSM data for the classification of LCZs with high generalization capability on a l

Author

Zhang, Guichen; Ghamisi, Pedram; Zhu, Xiao Xiang

Published

2019

Language

EN