Change Detection Types of Buildings in Aleppo Citadel Urban Area During Syrian Crisis Using Self-Organizing Maps Neural Networks and VHR Quickbird & Worldview-2 Satellite Images

Bashar Sabouh, Ahed Alboody, Mohamad Najib Salah, Ghadir Hmeidan

Abstract: A change detection in multi-temporal Very High Spatial Resolution (VHR) Satellite Images (SI) has a great importance in land survey and to document land change, residential areas changes and damage assessment caused by crises and natural disasters. We present a novel approach based on Self-Organizing Map (SOM) Artificial Neural Networks (ANN) to detect changes in multitemporal VHR SI based upon object level representation as a spatial vector and not only upon pixel level of image, from one hand. On other hand, identify of new types of changes, such as: Expanded Object (ExO), Erosion Object (ErO), Moved Object (MO), and not only identify as Stable Object (SO), Disappeared Object (DO), New Object (NO) as in most previous studies. We will explain in this way an application of the proposed method on VHR SI from two satellites QuickBird and Worldview-2 to detect and identify changes types in random residential area near Aleppo Citadel during Syrian Crisis period (from 2011 to 2016 for Aleppo City). The results and rates of correct changes detection are high, and the maps showed many discovered changes using our method.