Object-Based Method for Urban Extraction through Using Quick Bird Satellite Imagery, LiDAR Data and Digital Urban Geomatics Techniques

Abstract

Urban extraction mapping has become increasingly important in recent years and particularity extraction urban features based on remotely sensed data such as high-resolution imagery and LiDAR data. Though the researchers used the high spatial resolution image to extract urban area but he methods are still complexand still there are challenges associated with combining data that were acquired over differing time periods using inconsistent standards. So, this study will focus on the extraction of urban area based on an object-based classification method with integration of Quickbird satellite image and digital surface elevation (DSM) extracted from LiDAR data for the Rusafa city of Baghdad, Iraq. All the processes were done in eCognition and ArcGIS software for feature extraction and mapping, respectively. The overall methodological steps proposed in this research for the extraction of urban area using object-based method. In addition of that both the image data and LiDAR-derived DSM were integrated based on the eCognition software for extraction urban map of Rusafa city, Baghdad. Finally, the results indicated that the Artificial Neural Networks (ANN) model achieved the highest training and testing accuracies and performed the best compared to RF and Support Vector Machines (SVM) methods. And also, the results showed that the Artificial Neural Networks (ANN) had capability to extract the boundaries of the buildings and other urban features more accurately than the other two methods. This could be interpreted as the Artificial Neural Networks (ANN) model can learn complex features by the optimization process of the model and its multi-level feature extraction property.