This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed 'obje- oriented image analysis'. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).
Advances in earth observations sensors and GIScience have led to the emerging fields of Object-based Image Analysis (OBIA). The need for timely and accurate geo-spatial information is steadily increasing. This book discusses means, technologies and approaches related to the processing and analysis of multi-sensor, multi-resolution data with a focus on the generation, modelling and classification of objects. In high resolution images these objects are typically derived through regionalisation principles. The advancement of feature recognition and advanced image analysis techniques facilitate the extraction of thematic information for policy making support and informed decisions. The applications also address the integration of Geographic Information and the concurrent developments of GIScience and vanquish limitations of pixelwise image processing by exploiting image information context-driven and "intelligently".
