Building change detection through multi-scale GEOBIA approach by integrating deep belief networks with fuzzy ontologies

Abstract

ABSTRACTMonitoring and mapping urban changes is of great importance for the development, planning and management of the urban zone, especially in countries with a rapidly growing urban area. The aim of this paper was to develop a GEographic Object-Based Image Analysis (GEOBIA) approach, by integrating Deep Learning classification and Fuzzy Ontologies through multi-scale analysis, to monitor building changes in suburban areas of Greece. Three suburban areas of east Attica, Greece were selected as representative to test the methodology. For each area, one QuickBird and one WorldView 2 image, taken in 2006 and 2011, respectively, were employed. Three segmentation levels and a three-level class hierarchy were developed for the extraction process. Deep Belief Networks were employed on the lowest level of the segmentation hierarchy (Level 1) for an initial detection of areas of possible change. To detect the changes in building infrastructure, the classification result of Level 1 was refined based on interpretation rules, developed on the upper levels of the hierarchy (Level 2 and Level 3). Accuracy assessment indicated that 93.5% of the total number of changes were successfully detected, while the commission error was less than 20%.