Rule Inheritance in Object-based Image Classification for Urban Land Cover Mapping

Mapping land cover in urban areas helps understanding the complexity of the urban landscape and environments. High resolution image data effectively captures such urban complexities and offers great potential for mapping the urban features in detail. The land cover information derived from remote sensing data has proven its usefulness for a wide range of urban applications. The traditional pixel-based classifiers rely on spectral information only, thus unable to capture the complexity and diversity of urban environments inherent in the high resolution image data. On the other hand, the processing at object level with additional spatial and contextual information produces promising mapping results. Urban land cover mapping from high resolution imagery with additional geospatial data using objectbased fuzzy image classification techniques produced higher overall accuracy (90%). This paper explores the applicability and transferability of the fuzzy rule set developed over small areas to large areas with similar characteristics. The inheritance of rule set resulted into a slight decrease in overall classification accuracy (5%) that is due to certain new classes not represented previously. However, quickly updated rule set produced improved classification results and reduced processing time considerably.