Object - Based Classification and Applications in the Alpine Forest Environment

Context based classification is an important field of study in digital image analysis. Neighbouring pixels may possibly have almost equal grey values. The information of local homogenous patterns in a patch based landscape organisation has lead to studies, assuming the organisation of landscape patterns as being a complex of local spectral distributions. Image segmentation techniques are well known and an advanced algorithm is used in this study. New sensor generations meet the strong market demands from end-users, who are interested in image resolution that will help them observe and monitor their specific objects of interest. The mixed pixel problem and the increasing difficulties in the spectral analysis of high resolution images make it necessary to develop additional methods of classification. The key factor is to concentrate on the spectral properties of the objects of interest. This has important consequences. The increasing resolution (<5 m) leads to very complex spectral analysis. Fuzzy logic decision rules offer here a large reduction in complexity and a proper aid to group the spatial objects into meaningful classes. In this study, a new software package is used for object-based classification, developed by DELPHI2™ Creative Technologies. With this software, the segmentation procedure has to be set according to the image resolution and the scale of the expected objects. For foresters, the typical spatial object can range from forest-stands to crown surfaces. According to user preferences, objects of interest are grouped into a class. The fuzzy logic decision rules for class membership are the framework in which the expert knowledge has been embedded. The synergy of the spectral properties, the neighbourhood object influences and the expert knowledge lead to powerful ways of object membership decision rules. The fuzzy logic rules guarantee the transparency of the decision rules and reduce complexity to a condensed crisp set of end-membership functions. Integrating GIS layers is equally possible. The output of the object-based classification is typically a GIS layer.