Abstract:
With increasing urbanization, new technology is required to fulfil both human and environmental needs. At present, low-cost UAVs are used in surveying and mapping, and during the past few years, they have reached a level of practical requirements to allow the use of these systems as mapping platforms. Moreover, UAV based mapping provides required accuracy in line with cadastral laws and policies. Extraction of urban objects is a pre-requisite in various applications. In general, detection of buildings plays a major role in the field of remote sensing image processing, and also in urban planning and management. However, there is no ‘proper’ method developed to detect building features automatically from UAV images because there are usually too many details and distortions on the images. This paper presents an effective approach for extracting buildings from UAV images through the incorporation of orthophotographs and dense point clouds, rather than the traditional pixel based classification. In this method, different feature-based conditions are introduced with the help of a grid-based data structure for more accurate and quick extraction of building features. To verify the generality and advantage of the proposed method, the procedure is evaluated by performing experiments with a dataset acquired over the study area, which has a variety of building patterns and styles. The experimental results show an excellent performance in the detection of buildings, with an average overall accuracy greater than 80%. The final overall correctness and quality of building extraction are more than 80% and 65%, respectively. Therefore, there is a need to focus on more advanced conditions for building detection, to obtain optimum results.
