Rule-based land cover classification from very high-resolution satellite image with multiresolution segmentation

Multiresolution segmentation and rule-based classification techniques are used to classify objects from very high-resolution satellite images of urban areas. The major contribution of this research is the
development of rule sets and the identification of major features for satellite image classification
where the rule sets are transferable and the parameters are tunable for different types of imagery.
Additionally, the individual objectwise classification and principal component analysis help to
identify the required object from an arbitrary number of objects within images given ground truth
data for the training.