Data Fusion of Different Spatial Resolution Remote Sensing Images Applied to Forest-Type Mapping

Abstract

A data fusion method for land cover (LC) classification is proposed that combines remote sensing data at a fine and a coarse spatial resolution. It is a two-step approach, based on the assumption that some of the LC classes can be merged into a more generalized LC class. Step one creates a generalized LC map, using only the information available at the fine spatial resolution. In the second step, a new classifier refines the generalized LC classes to create distinct subclasses of its parent class, using the generalized LC map as a mask. This classifier uses all image information (bands) available at both fine and coarse spatial resolutions. We followed a simple data fusion technique by stacking the individual image bands into a multidimensional vector. The advantage of the proposed approach is that the spatial detail of the generalized LC classes is retained in the final LC map. The method has been designed for operational LC mapping over large areas. Within this paper, it is shown that the proposed data fusion approach increased the robustness of forest-type mapping within Europe. Robustness is particularly important when creating continental LC maps at fine spatial resolution. These maps become more popular now that remote sensing data at fine resolution are easier to access.