Interscale learning and classification for global HR/VHR image information extraction

An interscale learning paradigm for global HR/VHR image information extraction is presented. The paradigm relies on the information matching between a priori global knowledge and features derived from high resolution imagery to perform adaptive high resolution land classification. Unlike traditional machine learning techniques, this strategy avoids the costly collection of local training datasets and the local parameter tuning and it enables the full automation of the information extraction process.