Our new publication in ISPRS is accepted: “Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data”. This study addressed the classification of multi-temporal satellite data from RapidEye by considering different classifier algorithms and decision fusion. Four non-parametric classifier algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer… Read More


A new paper published recently in ISPRS presents our ongoing research on classification uncertainty in the context of multi-temporal object-based classification, based on support vector machines (SVM). It adds a contribution to the question of which factors influence the spatial distribution of classification uncertainty in land use maps.… Read More


A new paper published recently in PFG presents our ongoing research on the scale issue in the context of supervised image classification for crop monitoring. It extends a conceptional framework for the definition of pixel size requirements in land use classification, which we prevsiously published in Remote Sensing. It adds an analysis of the thematic… Read More