An automated object-based classification approach for updating Corine land cover data

In this paper, the framework of an object-based classification approach for land cover and land use classes is presented. Recently, there is an increasing demand for information on actual land cover resp. land use from planning, administration and science institutions. Remote sensing provides timely information products in different geometric and thematic scales. The effort to manually classify land use data is still very high. Therefore a new approach is required to incorperate automated image classification to human image understanding. The proposed approach couples object-based clasification technique - a rather new trend in image classification - with machine learning capacities (Support Vector Classifier) depending on information levels. To ensure spatial and spectral transferability of the classification scheme, the data has to be passed through several generalisation levels. The segmentation generates homogeneous and contiguous image objects. The hierarchical rule type uses direct and derived spectral attributes combined with spatial features and information extracted from the metadata. The identified land cover objects can be converted into the current CORINE classes after classification.