Author(s)

Alaeldin Suliman^*, Yun Zhang

Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada.

Building detection in very high resolution (VHR) remote sensing images is crucial for many urban planning and management applications. Since buildings are elevated objects, the incorporation of elevation data provides a mean to reliable detection. However, almost all existing methods of elevation-based building detection must first generate a normalized Digital Surface Model (nDSM). This model is generated by processes of extracting and subtracting terrain elevations from the DSM data. The generation of accurate nDSM is still a challenging task to some extent. This paper introduces a segment-based terrain filtering (SegTF) technique to filter out the terrain elevations directly using DSM elevations. This technique has four steps: elevation co-registration, image segmentation, slope calculation, and building detection. These steps of the developed technique were applied to a dataset that consisted of a VHR image and a corresponding DSM for detecting buildings. The result of the building detection was evaluated and found to be 100% correct with an overall detection quality of 93%. These values indicate a highly reliable and promising technique for mapping buildings in VHR images.

Building Detection, Terrain Filtering, Elevation Normalization, VHR Imagery

Suliman, A. and Zhang, Y. (2016) Segment-Based Terrain Filtering Technique for Elevation-Based Building Detection in VHR Remote Sensing Images. Advances in Remote Sensing, 5, 192-202. doi: 10.4236/ars.2016.53016.