BUILDING EXTRACTION FROM VERY HIGH-RESOLUTION SATELLITE IMAGES FOR MAP UPDATING IN EGYPT

Abstract

Robust building detection from satellite images has been a subject of interest for several decades. Very High Resolution (VHR) satellite images support the efficient extraction of manmade objects. The main aim of this paper is to present an approach for building extraction from VHR satellite images for map updating in Egypt. To achieve this aim, a comparison of pixel and object-based classification techniques has been applied. Then, different refinement processes based on shadow, context, shape, and Digital Surface Model (DSM) data are carried out. Two study areas from the VHR satellite images for Assuit and Sohag cities are used. A comparison of the classification techniques shows that the Maximum Likelihood Classifier (MLC) for pixel-based technique and Support Vector Machine (SVM) for object-based technique give the highest overall accuracy results. Refinement based on shadow, context, shape, and DSM information improves the overall accuracy with an average of 18%. Thus, the building extraction results can contribute significantly to update maps in Egypt.