Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications

While remote sensing has made enormous progress over recent years and a variety of sensors now deliver medium and high resolution data on an operational basis, a vast majority of applications still rely on basic image processing concepts developed in the early 70s: classification of single pixels in a multi-dimensional feature space. Although the techniques are well developed and sophisticated variations include soft classifiers, subpixel classifiers and spectral un-mixing techniques, it is argued that they do not make use of spatial concepts. Looking at high-resolution images it is very likely that a neighbouring pixel belongs to the same land cover class as the pixel under consideration. Algorithms in physics or mechanical engineering developed over the last twenty years successfully delineate objects based on context-information in an image on the basis of texture or fractal dimension. With the advent of high-resolution satellite imagery, the increasing use of airborne digital data and radar data the need for context-based algorithms and object-oriented image processing is increasing. Recently available commercial products reflect this demand. In a case study, ‘traditional’ pixel based classification methods and context-based methods are compared. Experiences are encouraging and it is hypothesised that object-based image analysis will trigger new developments towards a full integration of GIS and remote sensing functions. If the resulting objects prove to be ‘meaningful’, subsequent application specific analysis can take the attributes of these objects into account. The meaning of object dimension is discussed with a special focus on applications for environmental monitoring.