object based classification of large scale satellite images from complicated urban areas for land cover map production based on a new hierarchical model

land cover information is one of the most important prerequisite in urban management system. in this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. considering the complicated and dense urban areas in third world countries, object based approaches are suggested as an effective image processing technique. the purpose of this paper are the introduction of a new object based approach for classification of complicated urban area using high resolution satellite image and approaching to a standard and effective process of map generation by satellite images. this paper used a new approach to select the segmentation parameters and a new hierarchical classification model based on a rule based strategy is used to overcome the confusions between urban classes too. in this article an innovative hierarchical model is proposed for object-based classification of complicated urban areas. in this way, beside of feature space optimization in a multi scale analysis, rule based and fuzzy nearest neighbor approaches are used as the object-based classification strategies. the proposed method is implemented on an urban ikonos image where 84% and 87%overall accuracies are obtained for rule based and fuzzy nearest neighbor classification approaches respectively. the implementation of the devised algorithm on another ikonos image moved its general ability to other urban areas.