Deep learning approach for large scale land cover mapping based on remote sensing data fusion

In the paper we propose the methodology for solving the large scale classification and area estimation problems in the remote sensing domain on the basis of deep learning paradigm. It is based on a hierarchical model that includes self-organizing maps (SOM) for data preprocessing and segmentation (clustering), ensemble of multi-layer perceptrons (MLP) for data classification and heterogeneous data fusion and geospatial analysis for post-processing. The proposed methodology is applied for generation of high resolution land cover and land use maps for the territory of Ukraine from 1990 to 2010 and 2015.