Building Change Detection Based on a Gray-Level Co-Occurrence Matrix and Artificial Neural Networks

The recovery phase following an earthquake event is essential for urban areas with a significant number of damaged buildings. A lot changes can take place in such landscape within the buildings’ footprints, as total or partial collapses, debris removal and reconstruction. Remote sensing data methodologies considerably contribute to site monitoring. main objective this paper change detection building stock settlement Vrissa on Lesvos Island during after catastrophic 12 June 2017, through analysis processing UAV (unmanned aerial vehicle) images application Artificial Neural Networks (ANNs). More specifically, settlement’s by applying ANN Gray-Level Co-occurrence Matrix (GLCM) texture features orthophotomaps acquired UAVs was performed. For training ANN, GLCM were defined independent variable, while existence not structural buildings dependent assigning, respectively, values 1 0 (binary classification). trained based Levenberg–Marquardt algorithm, its ability detect evaluated basis condition, derived from binary classification. In conclusion, feature conjunction provide satisfactory results predicting accuracy almost 92%.